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Showing papers on "Neuro-fuzzy published in 2014"


Journal ArticleDOI
TL;DR: A new representation of the hesitant fuzzy linguistic term sets is presented by means of a fuzzy envelope to carry out the computing with words processes and can be directly applied to fuzzy multicriteria decision making models.

355 citations


Journal ArticleDOI
TL;DR: The objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type- 2 fuzzy sets that applies the proposed method to a supplier selection problem.
Abstract: The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1's, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.

318 citations


Journal ArticleDOI
TL;DR: The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets and showcases that the new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.
Abstract: Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating human's interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.

252 citations


Journal ArticleDOI
TL;DR: In this review, the application of genetic algorithms, particle swarm optimization and ant colony optimization are considered as three different paradigms that help in the design of optimal type-2 fuzzy controllers.

250 citations


Journal ArticleDOI
TL;DR: This tutorial paper explains four different mathematical representations for general type-2 fuzzy sets (GT2 FS) and demonstrates that for the optimal design of a GT2 FLS, one should use the vertical-slice representation of its GT2 FSs because it is the only one of the four mathematical representations that is parsimonious.
Abstract: The purpose of this tutorial paper is to make general type-2 fuzzy logic systems (GT2 FLSs) more accessible to fuzzy logic researchers and practitioners, and to expedite their research, designs, and use. To accomplish this, the paper 1) explains four different mathematical representations for general type-2 fuzzy sets (GT2 FSs); 2) demonstrates that for the optimal design of a GT2 FLS, one should use the vertical-slice representation of its GT2 FSs because it is the only one of the four mathematical representations that is parsimonious; 3) shows how to obtain set theoretic and other operations for GT2 FSs using type-1 (T1) FS mathematics (α- cuts play a central role); 4) reviews Mamdani and TSK interval type-2 (IT2) FLSs so that their mathematical operations can be easily used in a GT2 FLS; 5) provides all of the formulas that describe both Mamdani and TSK GT2 FLSs; 6) explains why center-of sets type-reduction should be favored for a GT2 FLS over centroid type-reduction; 7) provides three simplified GT2 FLSs (two are for Mamdani GT2 FLSs and one is for a TSK GT2 FLS), all of which bypass type reduction and are generalizations from their IT2 FLS counterparts to GT2 FLSs; 8) explains why gradient-based optimization should not be used to optimally design a GT2 FLS; 9) explains how derivative-free optimization algorithms can be used to optimally design a GT2 FLS; and 10) provides a three-step approach for optimally designing FLSs in a progressive manner, from T1 to IT2 to GT2, each of which uses a quantum particle swarm optimization algorithm, by virtue of which the performance for the IT2 FLS cannot be worse than that of the T1 FLS, and the performance for the GT2 FLS cannot be worse than that of the IT2 FLS.

238 citations


Journal ArticleDOI
TL;DR: A concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented and most of the applications in this review use interval type- 2 fuzzy logic, which is easier to handle and less computational expensive than generalized type-1 fuzzy logic.

229 citations


Journal ArticleDOI
01 May 2014-Energy
TL;DR: The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation and it is shown that the proposed intelligentEnergy management system is improving the performance of other existing systems.

161 citations


Journal ArticleDOI
TL;DR: It has been concluded from the study that the TSC-T–S fuzzy model provide reasonably accurate forecast with sufficient lead-time and a new model performance criterion termed as peak percent threshold statistics (PPTS) is proposed to evaluate the performance of a flood forecasting model.

156 citations


Journal ArticleDOI
TL;DR: An approach to linguistic hesitant fuzzy multi-attribute decision analysis is developed and models designed to obtain the optimal fuzzy measures and additive measures on an attribute set and on an ordered set are constructed.

131 citations


Journal ArticleDOI
TL;DR: This paper proposes a Takagi-Sugeno-Kang (TSK)-type-based self-evolving compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN), which produces smaller root-mean-square errors and converges more quickly in system modeling and noise cancellation problems.
Abstract: In this paper, a Takagi-Sugeno-Kang (TSK)-type-based self-evolving compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN) is proposed for system modeling and noise cancellation problems. A TSCIT2FNN uses type-2 fuzzy sets in an FNN in order to handle the uncertainties associated with information or data in the knowledge base. The antecedent part of each compensatory fuzzy rule is an interval type-2 fuzzy set in the TSCIT2FNN, where compensatory-based fuzzy reasoning uses adaptive fuzzy operation of a neural fuzzy system to make the fuzzy logic system effective and adaptive, and the consequent part is of the TSK type. The TSK-type consequent part is a linear combination of exogenous input variables. Initially, the rule base in the TSCIT2FNN is empty. All rules are derived according to online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by a variable-expansive Kalman filter algorithm to the reinforce parameter learning ability. The antecedent type-2 fuzzy sets and compensatory weights are learned by a gradient descent algorithm to improve the learning performance. The performance of TSCIT2FNN for the identification is validated and compared with several type-1 and type-2 FNNs. Simulation results show that our approach produces smaller root-mean-square errors and converges more quickly.

131 citations


Journal ArticleDOI
TL;DR: Noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.

Journal ArticleDOI
TL;DR: A simple interval type-2 FNN, which uses intervaltype-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang type in the consequent of the fuzzy rule, which yields fewer test errors and less computational complexity than other type-1 fuzzy systems.
Abstract: This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.

Journal ArticleDOI
TL;DR: A data-driven direct predictive controller (DDPC) is developed by utilizing the intermediate subspace matrices as local predictors and online update of the predictor is implemented on the multimodel structure to make the controller responsive to plant behavior variations.
Abstract: This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler–turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler–turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler–turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.

Proceedings ArticleDOI
01 Aug 2014
TL;DR: ANN provides a very exciting alternatives and other application which can play important role in today's computer science field and some Limitations also are mentioned.
Abstract: In this paper, An Artificial Neural Network or ANN, its various characteristics and business applications In this paper also show that “what are neural networks” and “Why they are so important in today's Artificial intelligence?” Because various advances have been made in developing intelligent system, some inspired by biological neural networks ANN provides a very exciting alternatives and other application which can play important role in today's computer science field There are some Limitations also which are mentioned

Journal ArticleDOI
TL;DR: An adaptive self-organizing fuzzy sliding-mode radial basis-function neural-network controller (ASFSRBNC) for robotic systems that solves the problem of an SFRBNC implementation in determining the stability of the system control and applies an adaptive law to modify the fuzzy consequent parameter of a fuzzy logic controller to manipulate a robotic system to improve its control performance.
Abstract: A self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) has been proposed to control robotic systems. The SFRBNC uses a radial basis-function neural-network (RBFN) to regulate the parameters of a self-organizing fuzzy controller (SOFC) to appropriate values in real time. This method solves the problem caused by the inappropriate selection of parameters in an SOFC. It also eliminates the dynamic coupling effects between degrees of freedom (DOFs) for robotic system control because the RBFN has coupling weighting regulation capabilities. However, its stability is difficult to demonstrate. To overcome the stability issue, this study developed an adaptive self-organizing fuzzy sliding-mode radial basis-function neural-network controller (ASFSRBNC) for robotic systems. The ASFSRBNC solves the problem of an SFRBNC implementation in determining the stability of the system control. It also applies an adaptive law to modify the fuzzy consequent parameter of a fuzzy logic controller to manipulate a robotic system to improve its control performance. The stability of the ASFSRBNC was proven using the Lyapunov stability theorem. From the experimental results of 6-DOF robotic control tests, the ASFSRBNC achieved better control performance than the SFRBNC as well as the SOFC.

Journal ArticleDOI
TL;DR: An NILM system with a novel hybrid classification technique that integrates Fuzzy C-Means clustering-piloting Particle Swarm Optimization with Neuro-Fuzzy Classification considering uncertainties is proposed and is feasible.
Abstract: In contrast with a centralized Home Energy Management System, a Non-intrusive Load Monitoring (NILM) system as an energy audit identifies power-intensive household appliances non-intrusively. In this paper, an NILM system with a novel hybrid classification technique is proposed. The novel hybrid classification technique integrates Fuzzy C-Means clustering-piloting Particle Swarm Optimization with Neuro-Fuzzy Classification considering uncertainties. In reality, household appliances or operation combinations of household appliances in a house field may be identified under similar electrical signatures. The ambiguities on electrical signatures extracted for load identification exist. As a result, the Fuzzy Logic theory is conducted. The ambiguities are addressed by the proposed novel hybrid classification technique for load identification. The proposed NILM system is examined in real lab and house environments with uncertainties. As confirmed in this paper, the proposed approach is feasible.

Journal ArticleDOI
TL;DR: This research addresses the application of fuzzy sets to design a multi-product, multi-period, closed-loop supply chain network and considers fuzzy/flexible constraints for fuzziness, fuzzy coefficients for lack of knowledge, and fuzzy goal of decision maker(s).
Abstract: Designing a logistic network is a strategic and critical problem that provides an optimal platform for the effective and efficient supply chain management In this research, we address the application of fuzzy sets to design a multi-product, multi-period, closed-loop supply chain network The presented supply chain includes three objective functions: maximization of profit, minimization of delivery time, and maximization of quality In the context of fuzzy mathematical programming, the paper jointly considers fuzzy/flexible constraints for fuzziness, fuzzy coefficients for lack of knowledge, and fuzzy goal of decision maker(s) According to fuzzy components considered, a fuzzy optimization approach is adopted to convert the proposed fuzzy multi-objective mixed-integer linear program into an equivalent auxiliary crisp model to obtain the relevant solutions Finally, the numerical experiments are given to demonstrate the significance of the proposed model as well as the solution approach

Journal ArticleDOI
TL;DR: The proposed novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC.
Abstract: This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

Journal ArticleDOI
TL;DR: This paper describes a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results and shows the new FPSO+FGA method to be superior with respect to both the individual evolutionary methods.

Journal ArticleDOI
01 Jun 2014
TL;DR: Simulation results of non-linear function identification using the IT2FNN for one and three variables and for the Mackey–Glass chaotic time series prediction are presented to illustrate that the proposed models have potential for real world applications.
Abstract: Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be important methods in real world applications, which range from pattern recognition, time series prediction, to intelligent control. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect or incomplete information. In this paper we are presenting several models of interval type-2 fuzzy neural networks (IT2FNNs) that use a set of rules and interval type-2 membership functions for that purpose. Simulation results of non-linear function identification using the IT2FNN for one and three variables and for the Mackey---Glass chaotic time series prediction are presented to illustrate that the proposed models have potential for real world applications.

Journal ArticleDOI
TL;DR: It has been shown that the reason for the superior control performance of the IT2-FPID under high levels of uncertainty and noise is not merely for its use of extra parameters, but rather its different way of dealing with the uncertainties and noise present in real world environments by comparing with a self-tuning T1-FP ID structure.

Journal ArticleDOI
TL;DR: A new model for multi-criteria CF using Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with subtractive clustering and Higher Order Singular Value Decomposition (HOSVD) is presented, used for dimensionality reduction for improving the scalability problem and ANFIS is used for extracting fuzzy rules from the experimental dataset.
Abstract: Collaborative Filtering (CF) is the most widely used prediction technique in recommender systems. It makes recommendations based on ratings that users have assigned to items. Most of the current CF recommender systems maintain only single user ratings inside the user-item ratings matrix. Multi-criteria based CF presents a possibility of providing accurate recommendations by considering the user preferences in multi aspects of items. However, in the multi-criteria CF, the user behavior about items' features is frequently subjective, imprecise and vague. These in turn induce uncertainty in reasoning and representation of items' features that exactly cannot be solved using crisp machine learning techniques. In contrast, approaches such as fuzzy methods instead of crisp methods can better solve the issue of uncertainty. In addition, fuzzy methods can predict the users' preference more accurately and even better alleviate the sparsity problem in overall rating by considering user perception about items' features. Apart from this, in the multi-criteria CF, users provide the ratings on different aspects (criteria) of an item in new dimensions; thereby, increasing the scalability problem. Appropriate dimensionality reduction techniques are thus needed to capture the high dimensions all together without reducing them into lower dimensions to reveal the latent associations among the components. This study presents a new model for multi-criteria CF using Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with subtractive clustering and Higher Order Singular Value Decomposition (HOSVD). HOSVD is used for dimensionality reduction for improving the scalability problem and ANFIS is used for extracting fuzzy rules from the experimental dataset, alleviating the sparsity problems in overall ratings and representing and reasoning the users' behavior on items' features. Experimental results on real-world dataset show that combination of two techniques remarkably improves the predictive accuracy and recommendation quality of multi-criteria CF.

Journal ArticleDOI
01 Oct 2014
TL;DR: A novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system and the result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.
Abstract: This paper presents a novel control approach of hybrid neuro-fuzzy (HNF) for load frequency control (LFC) of four-area power system. The advantage of this controller is that it can handle the non-linearities, and at the same time it is faster than other existing controllers. The effectiveness of proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in four area interconnected power system. Area-1 and area-2 consist of thermal reheat power plant whereas area-3 and area-4 consist of hydro power plant. Performance evaluation is carried out by using fuzzy, ANN, ANFIS and conventional PI and PID control approaches. The performances of the controllers are simulated using MATLAB/Simulink package. The result shows that intelligent HNF controller is having improved dynamic response and at the same time faster than ANN, fuzzy and conventional PI and PID controllers.

Journal ArticleDOI
TL;DR: This study directly associates fuzzy clustering with fuzzy modeling both in terms of conceptual and algorithmic linkages and identifies an interesting and direct linkage between the developed fuzzy models and a fundamental idea of encoding-decoding encountered in processing fuzzy sets and Granular Computing, in general.
Abstract: In this study, we propose a cluster-oriented development of fuzzy models. An overall design process is focused on an efficient usage of fuzzy clustering, Fuzzy C-Means (FCM), in particular, to form information granules—clusters that are used in the construction of the fuzzy model. Fuzzy models are regarded as mappings from information granules expressed in the input and output spaces. This position motivates us to look at the development of the models through the perspective of the construction and efficient usage of information granules. This study directly associates fuzzy clustering with fuzzy modeling both in terms of conceptual and algorithmic linkages. The augmented FCM method is formed predominantly for modeling purposes so that a balance between the structural content present in the input and output spaces is achieved and this way the performance of the resulting fuzzy model is optimized. It is shown that the cluster-oriented modeling gives rise to the Mamdani-like fuzzy rules and a zero-order Takagi–Sugeno model (under a certain decoding scheme). We identify an interesting and direct linkage between the developed fuzzy models and a fundamental idea of encoding–decoding (or granulation–degranulation) encountered in processing fuzzy sets and Granular Computing, in general. Furthermore, refinements of zero-order fuzzy models are investigated leading to first-order fuzzy models with linear functions standing in the conclusions of the rules. A series of experiments is reported where we used synthetic and real-world data using which an issue of generalization capabilities is elaborated in detail.

Journal ArticleDOI
TL;DR: A novel high-order fuzzy time series model which overcomes the drawback of fuzzification and applies an artificial neural network to compute the complicated fuzzy logical relationships and uses the adaptive expectation model to adjust the forecasting during the defuzzification procedure.

Journal ArticleDOI
TL;DR: A self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed and is used to model nonlinear systems.
Abstract: In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.

Journal ArticleDOI
TL;DR: The aim of the proposed method is not only to achieve appropriate accuracy of the model, but also to ensure the possibility of interpretability of the knowledge within it, by appropriate selection of operational criteria applied to evolutionary model creation.

Journal ArticleDOI
TL;DR: To establish the efficiency of the proposed classifier the presentation of the anticipated genetic-fuzzy classifier is evaluated with quantitative, qualitative and comparative analysis, and AGFS obtained better accuracy when compared to the existing systems.

BookDOI
16 Jul 2014
TL;DR: These three volumes (CCIS 442, 443, 444) constitute the proceedings of the 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2014, held in Montpellier, France, July 15-19, 2014.
Abstract: These three volumes (CCIS 442, 443, 444) constitute the proceedings of the 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2014, held in Montpellier, France, July 15-19, 2014. The 180 revised full papers presented together with five invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on uncertainty and imprecision on the web of data; decision support and uncertainty management in agri-environment; fuzzy implications; clustering; fuzzy measures and integrals; non-classical logics; data analysis; real-world applications; aggregation; probabilistic networks; recommendation systems and social networks; fuzzy systems; fuzzy logic in boolean framework; management of uncertainty in social networks; from different to same, from imitation to analogy; soft computing and sensory analysis; database systems; fuzzy set theory; measurement and sensory information; aggregation; formal methods for vagueness and uncertainty in a many-valued realm; graduality; preferences; uncertainty management in machine learning; philosophy and history of soft computing; soft computing and sensory analysis; similarity analysis; fuzzy logic, formal concept analysis and rough set; intelligent databases and information systems; theory of evidence; aggregation functions; big data - the role of fuzzy methods; imprecise probabilities: from foundations to applications; multinomial logistic regression on Markov chains for crop rotation modelling; intelligent measurement and control for nonlinear systems.

Journal ArticleDOI
TL;DR: This method is based on the calculation of sets of parameters of a PV module in different operating conditions, by means of a Neuro-Fuzzy approach and is able to discern between normal and faulty operation conditions and with the same defective existence of noise and disturbances.