Showing papers on "Membership function published in 2020"
TL;DR: By online estimating the unknown parameters in upper bounds of cyberattacks and external disturbances, an adaptive sliding mode controller is synthesized such that the reachability of the prescribed sliding surface can be guaranteed and the effect of cyber attacks on the system performance can be effectively attenuated.
Abstract: This paper focuses on the sliding mode control (SMC) problem of interval type-2 (IT2) fuzzy systems subject to the unmeasurable state and cyberattacks. A key issue is how to design a state observer under the constraint that only the bounds of membership functions are known. To this end, this paper introduces two weighting factors to construct a new membership function. Besides, the concept of input-to-state stability (ISS) is utilized to deal with the residual term resulting from the cyberattacks and external disturbances. The sufficient condition is established such that the sliding mode dynamics and the estimated error dynamics are input-to-state stable. Furthermore, by online estimating the unknown parameters in upper bounds of cyberattacks and external disturbances, an adaptive sliding mode controller is synthesized such that the reachability of the prescribed sliding surface can be guaranteed and the effect of cyberattacks on the system performance can be effectively attenuated. Finally, the validity of the proposed method is illustrated by a mass–spring–damper system.
97 citations
TL;DR: A novel fuzzy observer-based repetitive controller is designed to deal with the periodic tracking control problem for nonlinear systems and the fuzzy Lyapunov functional with an additional separation parameter and the augmented fuzzy matrix technique are proposed such that the delay-dependent stability condition of the closed-loop system in the form of linear matrix inequality is obtained with less conservatism.
Abstract: This article is concerned with the periodic tracking control problem for nonlinear systems. First, the Takagi–Sugeno (T–S) fuzzy model is employed to describe the nonlinear control systems. Second, considering the partly unmeasurable states of the system, a novel fuzzy observer-based repetitive controller, which is the mixed controller of the fuzzy observer-based controller and the fuzzy repetitive controller, is designed to deal with the periodic tracking control problem. To reduce the conservatism and increase the feasible solution space of the stabilization conditions, a new fuzzy relaxed matrix technique is developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Then, the fuzzy Lyapunov functional with an additional separation parameter and the augmented fuzzy matrix technique (the interactions of fuzzy observer subsystems) are proposed such that the delay-dependent stability condition of the closed-loop system in the form of linear matrix inequality is obtained with less conservatism. It is worth noting that due to introducing an additional parameter in the fuzzy Lyapunov functional, the fuzzy controller and fuzzy observer can be separately designed, which largely enhances the flexibility of design with low computational complexity. Finally, three examples are provided to illustrate the effectiveness and less conservatism of the proposed method.
88 citations
TL;DR: A global sensitivity analysis indicated that the potassium permanganate index (CODMn) and Secchi disc (SD) are the most sensitive factors in the developed approach and a range for the coefficient P value in the membership function was recommended.
75 citations
TL;DR: A new MCDM method based on the COMET method is developed by using the concept of NIVTFNs, and the solution of the proposed method, as interval preference, is compared with the results obtained in the Technique for Order of Preference by Similarity to Ideal solution (TOPSIS) method.
Abstract: Multi-criteria decision-making (MCDM) plays a vibrant role in decision-making, and the characteristic object method (COMET) acts as a powerful tool for decision-making of complex problems. COMET technique allows using both symmetrical and asymmetrical triangular fuzzy numbers. The COMET technique is immune to the pivotal challenge of rank reversal paradox and is proficient at handling vagueness and hesitancy. Classical COMET is not designed for handling uncertainty data when the expert has a problem with the identification of the membership function. In this paper, symmetrical and asymmetrical normalized interval-valued triangular fuzzy numbers (NIVTFNs) are used for decision-making as the solution of the identified challenge. A new MCDM method based on the COMET method is developed by using the concept of NIVTFNs. A simple problem of MCDM in the form of an illustrative example is given to demonstrate the calculation procedure and accuracy of the proposed approach. Furthermore, we compare the solution of the proposed method, as interval preference, with the results obtained in the Technique for Order of Preference by Similarity to Ideal solution (TOPSIS) method (a certain preference number).
66 citations
TL;DR: The obtained results confirmed the superiority of the proposed MBA in designing the fuzzy PID-LFC as it provides less error with best statistical parameters compared to the others.
Abstract: This paper presents a novel optimal fuzzy proportional–integral–derivative (fuzzy PID) controller for load frequency control (LFC) designed by a proposed approach of mine blast algorithm (MBA) for multi-interconnected areas The system includes reheat thermal connected power systems with the effect of the governor dead zone and turbine generation rate constraint nonlinearity The proposed approach is used to determine the optimal parameters of the fuzzy PID controller to minimize the integral time absolute error The proposed controller is inserted in multi-interconnected power systems which are built in Simulink/MATLAB library; triangular membership function is used for fuzzy PID controller Additionally, the optimum adjustment of the fuzzy PID controller parameters under contracted scenario for large step demands and disturbances is investigated by MBA The obtained results are compared to those obtained via antlion optimizer, artificial bee colony, hybrid differential evolution particle swarm optimization and hybrid PSO pattern search algorithm The obtained results confirmed the superiority of the proposed MBA in designing the fuzzy PID-LFC as it provides less error with best statistical parameters compared to the others
62 citations
TL;DR: G granular ball neighborhood rough sets (GBNRS) is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets.
Abstract: Feature reduction is an important aspect of Big Data analytics on today's ever-larger datasets. Rough sets are a classical method widely applied in attribute reduction. Most rough set algorithms use the priori domain knowledge of a dataset to process continuous attributes through using a membership function. Neighborhood rough sets (NRS) replace the membership function with the concept of neighborhoods, allowing NRS to handle scenarios where no a priori knowledge is available. However, the neighborhood radius of each object in NRS is fixed, and the optimization of the radius depends on grid searching. This diminishes both the efficiency and effectiveness, leading to a time complexity of not lower than O(N
$^2$
). To resolve these limitations, granular ball neighborhood rough sets (GBNRS), a novel NRS method with time complexity O(N), is proposed. GBNRS adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility in comparison to standard NRS methods. GBNRS is compared with the current state-of-the-art NRS method, FARNeMF, and find that GBNRS obtains both higher performance and higher classification accuracy on public benchmark datasets. All code has been released in the open source GBNRS library at http://www.cquptshuyinxia.com/GBNRS.html.
59 citations
TL;DR: The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing.
52 citations
TL;DR: The stability analysis and control synthesis of interval type-2 (IT2) polynomial-fuzzy-model-based networked control systems are investigated under the event-triggered control framework and the intrinsic mismatched issue of the premise variables of the fuzzy model and controllers due to theevent-triggering mechanism is handled by the MFD approach.
Abstract: In this article, the stability analysis and control synthesis of interval type-2 (IT2) polynomial-fuzzy-model-based networked control systems are investigated under the event-triggered control framework. The nonlinear dynamics in the plant is efficiently represented by an IT2 polynomial fuzzy model that the IT2 membership functions are utilized to capture the uncertainties in the plant. An event-triggered IT2 polynomial fuzzy controller is then designed to stabilize the nonlinear model subject to uncertainties. The stability conditions of the closed-loop control system are summarized in the form of sum-of-squares. Under the imperfectly premise matching (IPM) concept, the membership-function-dependent (MFD) approach is applied to endow the polynomial fuzzy controllers with more flexibility in terms of number of rules and premise membership functions. In the MFD approach under the IPM concept, both the number of rules and the shape of membership functions in the fuzzy models and controllers can be different. Also, the information of IT2 membership functions of the polynomial fuzzy model and controller is considered and adopted to further relax the stability conditions. Furthermore, the intrinsic mismatched issue of the premise variables of the fuzzy model and controllers due to the event-triggering mechanism is handled by the MFD approach. A detailed simulation example is provided to verify the effectiveness of the proposed event-based control strategy.
50 citations
TL;DR: Type-2 fuzzy sets (T2 FSs) are used to model the timing constraints in RTESs and novel algorithms for membership function generation and calculation of fuzzy earliness are introduced to solve this problem.
44 citations
TL;DR: This work proposes a novel decision algorithm based on q-ROF set to deal with multiple heterogeneous relationships among membership functions and criteria using interactive operators and Maclaurin symmetric mean (MSM) operators.
Abstract: Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods under q-ROF environment have been developed in recent years. However, most existing studies merely concerned about the relationship between the criteria but they have not investigated the interactions between membership function and non-membership function. To explore the multiple heterogeneous relationships among membership functions and criteria, we propose a novel decision algorithm based on q-ROF set to deal with these using interactive operators and Maclaurin symmetric mean (MSM) operators. Specifically, the new interaction laws in the membership pairs of q-ROF sets are explained, and their properties are analyzed as the initial stage. Then, taking into account the influence of two or more factors on decision analysis, a q-ROF interaction Maclaurin symmetry mean (q-ROFIMSM) operator is formed based on the proposed interaction law to identify these factors' interrelationship. Thirdly, based on the proposed operator with q-ROF information, a MCDM algorithm is developed and illustrated by numerical examples. An analysis of the feasibility, sensitivity, and superiority of the proposed framework is provided to validate our proposed method.
43 citations
TL;DR: A membership function-dependent performance index is proposed, and an observer-based feedback control scheme is presented in the form of linear matrix inequalities to guarantee better control effects in practical engineering applications.
Abstract: This article focuses on disturbance attenuation problem for discrete-time Takagi–Sugeno (T–S) fuzzy systems. First, a membership function-dependent $H_{\infty }$ performance index is proposed, by which, different performance can be achieved for local subsystems. Then, the better performance may be obtained for the T–S fuzzy system that works on a particular local subsystem most of the time. It may guarantee better control effects in practical engineering applications. With the help of the newly defined performance index and fuzzy Lyapunov functions, an observer-based feedback control scheme is presented in the form of linear matrix inequalities. In simulation, the performance index obtained by this article can be improved by about $10\%$ than that obtained by the traditional $H_{\infty }$ control method.
01 Sep 2020
TL;DR: The research paper presents implementation of a fuzzy rule and membership function-based fuzzy-aided PID controller for automatic generation control (AGC) in multiarea nonlinear power system and it has been revealed that proposed M-SCA-tuned fuzzy- aided PID controllers exhibits better performances through different deviated responses for AGC analysis.
Abstract: The research paper presents implementation of a fuzzy rule and membership function-based fuzzy-aided PID controller for automatic generation control (AGC) in multiarea nonlinear power system. At the initial stage of this proposed work, a three-area nine-unit installed interconnected network is considered for developing different dynamic responses in response to AGC analysis. A modified approach named modified sine cosine algorithm (M-SCA) is proposed for tuning the gain parameters of the above-proposed fuzzy controller to produce close optimum gain values. The proposed modified algorithm is developed from its original sine cosine algorithm by improving and updating few equations which is capable of making the balance between exploration and exploitation levels of this algorithm and improving the updating quality of iteration. To impose supremacy of M-SCA technique, it is examined through convergence curves and its performance is compared with host sine cosine algorithm, genetic algorithm, and particle swarm optimization algorithm. For controller supremacy analysis, the performance of the proposed fuzzy-aided PID controller is compared with conventional I, PI, and PID controllers, and it has been revealed that proposed M-SCA-tuned fuzzy-aided PID controller exhibits better performances through different deviated responses for AGC analysis. To demonstrate most standard and supremacy of proposed approaches, finally these are tested through a five-area ten-unit system considering some physical nonlinear constraints like generation rate constraint, governor dead band, boiler dynamics and time delay. At the final observation level, the proposed fuzzy controller has gone through different sensitivity analyses with variation of different system parametric conditions and different load conditions.
TL;DR: A new interval type-2 fuzzy (IT2F) multicriteria decision-making method based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), which can effectively handle the degree of uncertainty in group decision making and assist in the selection of the most feasible and optimal industrial asset maintenance strategy.
Abstract: The selection of an optimal maintenance strategy is one of the principal strategic decisions that must be taken in many contexts in order to maintain an asset with minimum deterioration and to deliver maximum output with high quality. When considering maintenance cost, reliability, and safety level of industrial assets, decision makers must select an appropriate maintenance strategy, preferably, with a known degree of uncertainty. This article utilizes a new interval type-2 fuzzy (IT2F) multicriteria decision-making method based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), which can effectively handle the degree of uncertainty in group decision making and assist in the selection of the most feasible and optimal industrial asset maintenance strategy. The proposed method is compared with the results obtained from the conventional AHP-TOPSIS and type-1 fuzzy AHP-TOPSIS—the rank obtained from IT2F-AHP-TOPSIS is identical to those methods and can be used as an effective alternative to the type-1 fuzzy method. Compared with conventional AHP-TOPSIS and type-1 fuzzy AHP, IT2F-AHP-TOPSIS has the added advantage that it allows decision makers to define the membership function with greater flexibility and is able to handle uncertainty during decision making.
TL;DR: CT2TFDNN not only provides a fast chaotic fuzzy-neuro deep learning and forecast solution, but also successfully resolves the massive data overtraining and deadlock problems, which are usually imposed by traditional recurrent neural networks using classical sigmoid-based activation functions.
Abstract: Over the years, financial engineering ranging from the study of financial signals to the modeling of financial prediction is one of the most exciting topics for both academia and financial community. With the flourishing AI technology in the past 20 years, various hybrid intelligent financial prediction systems with the integration of neural networks, chaos theory, fuzzy logic, and genetic algorithms have been proposed. An interval type-2 fuzzy logic system (IT2FLS) with its remarkable capability for the modeling of highly uncertain events and attributes provides a perfect tool to interpret various financial phenomena and patterns. In this paper, the author proposes a chaotic type-2 transient-fuzzy deep neuro-oscillatory network with retrograde signaling (CT2TFDNN) for worldwide financial prediction. With the extension of author's original work on Lee oscillator—a chaotic discrete-time neural oscillator with profound transient-chaotic property—CT2TFDNN provides: effective modeling of an IT2FLS with a chaotic transient-fuzzy membership function; and effective time-series network training and prediction using a chaotic deep neuro-oscillatory network with retrograde signaling. CT2TFDNN not only provides a fast chaotic fuzzy-neuro deep learning and forecast solution, but also successfully resolves the massive data overtraining and deadlock problems, which are usually imposed by traditional recurrent neural networks using classical sigmoid-based activation functions. From the implementation perspective, CT2TFDNN is integrated with 2048 trading-day time-series financial data and top-10 major financial signals as fuzzy financial signals for the real-time prediction of 129 worldwide financial products that consists of: nine major cryptocurrencies, 84 worldwide forex, 19 major commodities, and 17 worldwide financial indices.
TL;DR: In this article, a fuzzy model with triangular membership function utilizing bisector method of defuzzification performs better, compared to triangular and trapezoidal membership function with centroid, bisector and mean of maxima (MOM) methods for defuzzifying.
TL;DR: An accurate numeric-analytic algorithm is proposed, based upon the use of the residual power series, to investigate the fuzzy approximate solution for a nonlinear fuzzy Duffing oscillator, along with suitable uncertain guesses under strongly generalized differentiability.
Abstract: The mathematical structure of some natural phenomena of nonlinear physical and engineering systems can be described by a combination of fuzzy differential equations that often behave in a way that cannot be fully understood. In this work, an accurate numeric-analytic algorithm is proposed, based upon the use of the residual power series, to investigate the fuzzy approximate solution for a nonlinear fuzzy Duffing oscillator, along with suitable uncertain guesses under strongly generalized differentiability. The proposed approach optimizes the approximate solution by minimizing a residual function to generate r-level representation with a rapidly convergent series solution. The influence, capacity, and feasibility of the method are verified by testing some applications. Level effects of the parameter r are given graphically and quantitatively, showing good agreement between the fuzzy approximate solutions of upper and lower bounds, that together form an almost symmetric triangular structure, that can be determined by central symmetry at r = 1 in a convex region. At this point, the fuzzy number is a convex fuzzy subset of the real line, with a normalized membership function. If this membership function is symmetric, the triangular fuzzy number is called the symmetric triangular fuzzy number. Symmetrical fuzzy estimates of solutions curves indicate a sense of harmony and compatibility around the parameter r = 1. The results are compared numerically with the crisp solutions and those obtained by other existing methods, which illustrate that the suggested method is a convenient and remarkably powerful tool in solving numerous issues arising in physics and engineering.
TL;DR: The obtained results are very close to the results from pre-existing approaches which confirm that the proposed approach is a more realistic alternative for the study of system reliability in intuitionistic fuzzy environment when quantitative failure data of system components are not known.
Abstract: In quantitative fault tree analysis of a system, exact failure probability values of components are utilized to calculate the failure probability of the system. However, in many real world problems, it is problematic to get precise and sufficient failure data of system components due to insufficient or imprecise information about components, changing environment or new components. A methodology has already been developed by employing fuzzy set theory for the system reliability evaluation by utilizing qualitative failure data of system components when quantitative failure data of components are inaccessible or insufficient. This paper extends the concept of fuzzy set to intuitionistic fuzzy set and proposes a novel approach to evaluate system failure probability using intuitionistic fuzzy fault tree analysis with qualitative failure data of system components. The qualitative failure data such as expert opinions are collected as linguistic terms. These linguistic terms are then quantified by triangular intuitionistic fuzzy numbers in form of membership function and non-membership function. Additionally, a method is developed for combining the different opinions of experts. To illustrate the applicability of proposed approach, a case study of the crude oil tank fire and explosion accident is performed. The obtained results are very close to the results from pre-existing approaches which confirm that the proposed approach is a more realistic alternative for the study of system reliability in intuitionistic fuzzy environment when quantitative failure data of system components are not known. To help decision makers for improving the security execution of the crude oil tank system, importance measures including Fussell-Vesely importance and cut sets importance are also executed.
TL;DR: A novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs) and shows that the BCLM control system significantly reduces fuel consumption and is validated by a hardware-in-the-loop test.
Abstract: This article proposes a novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs). This mechanism allows continuous competition between two fuzzy logic controllers during real-world driving. The leading controller will have the regulatory function of the supervisory control system. First, the configuration of the HEV model and its FL-based control system are analyzed. Second, the algorithm of chaos-enhanced accelerated particle swarm optimization (CAPSO) is developed for back-to-back learning of the membership function. Third, based on fuel-prioritized cost functions, the regulation of competitive assessment is designed to select a controller with a better fuel economy. Finally, the competitive performance of using the CAPSO algorithm is contrasted with other swarm-based methods and the BCLM-driven control system is validated by a hardware-in-the-loop test. The results demonstrate that the BCLM control system significantly reduces fuel consumption, at least 9% from charge sustaining and charge depleting based, and at least 7% from conventional FL-based systems.
TL;DR: A Neuro-Fuzzy C-Means Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach.
TL;DR: A new combination of CFD and the ANFIS method enables us to learn flow and temperature distribution throughout the domain thoroughly, and eventually predict the flow characteristics in short computational time, as well as compare the pattern recognition in the two systems.
Abstract: In this study, a square cavity is modeled using Computational Fluid Dynamics (CFD) as well as artificial intelligence (AI) approach. In the square cavity, copper (Cu) nanoparticle is the nanofluid and the flow velocity characteristics in the x-direction and y-direction, and the fluid temperature inside the cavity at different times are considered as CFD outputs. CFD outputs have been assessed using one of the artificial intelligence algorithms, such as a combination of neural network and fuzzy logic (ANFIS). As in the ANFIS method, we have a non-dimension procedure in the learning step, and there is no issue in combining other characteristics of the flow and thermal distribution beside the x and y coordinates, we combine two coordinate parameters and one flow parameter. This ability of method can be considered as a meshless learning step that there is no instability of the numerical method or limitation of boundary conditions. The data were classified using the grid partition method and the MF (membership function) type was dsigmf (difference between two sigmoidal membership functions). By achieving the appropriate intelligence in the ANFIS method, output prediction was performed at the points of cavity which were not included in the learning process and were compared to the existing data (the results of the CFD method) and were validated by them. This new combination of CFD and the ANFIS method enables us to learn flow and temperature distribution throughout the domain thoroughly, and eventually predict the flow characteristics in short computational time. The results from AI in the ANFIS method were compared to the ant colony and fuzzy logic methods. The data from CFD results were inserted into the ant colony system for the training process, and we predicted the data in the fuzzy logic system. Then, we compare the data with the ANFIS method. The results indicate that the ANFIS method has a high potentiality compared to the ant colony method because the amount of R in the ANIFS system is higher than R in the ant colony method. In the ANFIS method, R is equal to 0.99, and in the ant colony method, R is equal to 0.91. This shows that the ant colony needs more time for both the prediction and training of the system. Also, comparing the pattern recognition in the two systems, we can obviously see that by using the ANFIS method, the predictions completely match the target points. But the other method cannot match the flow pattern and velocity distribution with the CFD method.
TL;DR: A weighted spatial Fuzzy C-Means (wsFCM) segmentation method is proposed that considered the spatial information of image and is compared with existing state-of-the-art methods in handling noise and intensity inhomogeneity.
24 Jul 2020
TL;DR: An improved method for finding solutions for neuro-fuzzy expert systems of analysis of the electronic environment and artificial neural networks that are evolving and learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function is developed.
Abstract: Nowadays, artificial intelligence has entered into all spheres of our life. The system of analysis of the electronic environment is not an exception. However, there are a number of problems in the analysis of the electronic environment, namely the signals. They are analyzed in a complex electronic environment against the background of intentional and natural interference. Also, the input signals do not match the standards due to the influence of different types of interference. Interpretation of signals depends on the experience of the operator, the completeness of additional information on a specific condition of uncertainty. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment and artificial neural networks. Their advantage is also the ability to work in real time and quick adaptation to specific situations. These circumstances cause uncertainty in the conditions of the task of signal recognition and fuzzy statements in their interpretation, when the additional involved information may be incomplete and the operator makes decisions based on their experience.
That is why, in this article, an improved method for finding solutions for neuro-fuzzy expert systems of analysis of the electronic environment is developed.
Improving the efficiency of information processing (reducing the error) of evaluation is achieved through the use of neuro-fuzzy artificial neural networks that are evolving and learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. High efficiency of information processing is also achieved through training in the architecture of artificial neural networks by taking into account the type of uncertainty of the information that has to be assessed and work with clear and fuzzy products. This reduces the computational complexity of decision-making and absence of accumulation of an error of training of artificial neural networks as a result of processing of the arriving information on an input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % on the efficiency of the processing information
TL;DR: This paper focuses on the study of natural fault detection of angular contact ball bearing using frequency domain signal processing based on acoustic emission signals and shows that the classification using the ANFIS network based on the FDA features has less error compared to the features extracted from the PCA method.
TL;DR: A novel trust evaluation framework is proposed that employs water cycle algorithm (WCA) for automatic tuning of the rule set and membership function for the decision variable to route the packet in an adaptable manner and is clear that the RS and MF generated by the proposed model is small and compact enough to provide reliable routing in SGCN.
Abstract: Finding an optimal route for reliable data delivery in the smart grid communication network (SGCN) is a challenging task due to its dynamic nature. Even though the rule set (RS) and membership function (MF) framed intuitively in our previous fuzzy logic (FL) approach performs the trusted routing satisfactorily, it consumes computational memory and reduces the energy efficiency of the node. To address this issue, in this article, we proposed a novel trust evaluation framework that employs water cycle algorithm (WCA) for automatic tuning of the rule set and membership function for the decision variable to route the packet in an adaptable manner. Variables like distance, link stability, and node honesty are considered for evaluation using WCA in three iterative processes, namely, exploitation, evaporation, and raining to find the near optimal if–then rules and points for the membership functions. An experimental setup is created using Network Simulator-2 (NS2) to evaluate the performance of the proposed trusted routing algorithm in SGCN. Extensive experiments are conducted for three cases, namely, 1) evaluating RS with fixed MF; 2) evaluating MF with fixed RS; and 3) combined evaluation of MF and RS to evaluate the performance of the proposed model. From the simulation results, it is clear that the RS and MF generated by the proposed model is small and compact enough to provide reliable routing in SGCN.
TL;DR: An improved genetic algorithm is employed to optimize the initial expert empirical fuzzy rules, which avoids the traditional method from falling into the local optimal solution in the process of optimization.
TL;DR: The experimental results indicate that the proposed method for gas-fired combustion based on the image processing technology has better performance in identifying different combustion situations in a gaseous flame and is superior to the other algorithms.
TL;DR: A gradient-based method to approximate a fuzzy set through a trapezoidal fuzzy set (TFS) by adding some constraints in the formulated optimization problem, so that the major characteristics of the fuzzy set could be preserved and the form of the optimized result as a TFS is guaranteed.
Abstract: In this paper, we propose a gradient-based method to approximate a fuzzy set through a trapezoidal fuzzy set (TFS). By adding some constraints in the formulated optimization problem, the major characteristics of the fuzzy set such as the core, the major part of the support, and the shape of the membership function could be preserved; also the form of the optimized result as a TFS is guaranteed. We regard the optimized TFS as the “skeleton” (blueprint) of the original fuzzy set. Based on this skeleton, we further extend the TFS to a higher type, that is, an interval type-2 TFS (IT2 TFS), so that more information about the original fuzzy set could be captured but the number of the parameters used to describe the original fuzzy set is still maintained low (nine parameters are required for an IT2 TFS). The principle of justifiable granularity is used to ensure that the formed type-2 information granule exhibits a sound interpretation. Both synthetic fuzzy sets and those constructed by the fuzzy ${C}$ -means algorithm applied to the publicly available data have been used to demonstrate the usefulness of the proposed approximation methods.
TL;DR: Experiments on the real autonomous vehicle platform ‘Xinda’ and performance comparison with other fault detectors validate the effectiveness of these methods and the usability of the fault detection and diagnosis system.
Abstract: An accurate fault detection and diagnosis system is of great importance for autonomous vehicles to prevent the potential hazardous situations. In this paper, we propose a fault detection and diagnosis system based on hybrid approaches. First, to detect the state faults of the autonomous vehicle, One-Class Support Vector Machine (SVM) method is adopted to train the boundary curve which separates the safe domain and unsafe domain. Meanwhile, a Kalman filter observer is designed based on the linear kinematic vehicle bicycle model to predict the current position of the vehicle, and after obtaining the residuals between prediction and measurement, Jarque-Bera test is applied to check the normality of the residuals probability distribution to monitor whether the trajectory deviates. Furthermore, we design a fuzzy system to distinguish the types of the detected faults based on a modified neutral network, in which a membership function layer is added after the input layer. With the strong self-learning ability of neutral network, the initial membership function of the fuzzy system is updated through black box test and can indicate the probability of each fault type. Experiments on the real autonomous vehicle platform ‘Xinda’ and performance comparison with other fault detectors validate the effectiveness of these methods and the usability of the fault detection and diagnosis system.
TL;DR: A quantum-inspired lightning search algorithm with enhanced performance is presented to avoid exhaustive conventional heuristic procedures when obtaining MFs and the accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads.
Abstract: Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.
TL;DR: To ensure the asymptotic stability of NCSs, valuable theorems are derived by defining Lyapunov functions which involve time-delay information and LMI-based membership-function-dependent stability conditions are developed to reduce the conservativeness of the imperfect premise matching.
Abstract: This paper is mainly concerned with the stabilization problem for nonlinear networked control systems (NCSs) with a two-terminal event-triggered mechanism. Firstly, Takagi–Sugeno (T–S) fuzzy models are introduced as the presentation of the nonlinear object. A two-terminal event-triggered mechanism, which adopts the relative event-triggered conditions as the communication mode of data transmission, is distributed in the controller-to-actuator link besides sensor-to-controller link in order to reduce congestion on network servers. By taking different membership function information from the T–S fuzzy model into account, the dynamic output feedback controller is designed under imperfect premise matching to construct closed-loop model. Secondly, to ensure the asymptotic stability of NCSs, valuable theorems are derived by defining Lyapunov functions which involve time-delay information. Thirdly, using boundary information of membership functions, LMI-based membership-function-dependent stability conditions are developed to reduce the conservativeness of the imperfect premise matching, from which the explicit form of controller gain matrices is obtained. Finally, one practical example illustrates the reliability of derived results.