Showing papers on "Neuro-fuzzy published in 2013"

Neural networks, fuzzy logic, and genetic algorithms : synthesis and applications

Abstract: A Study of Adaptive Neural Network Control System. Zhong, Heng Design of Fuzzy Logic Controller Based on Differential Evolution Algorithm. Shuai, Li (et al.). Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis. Fuzzy Logic and Neural Networks: Basic Concepts and Applications. logic genetic by rajasekaran ebook. srajasekaran and ga vijayalakshmi pai neural networks. MODERN MAGNETIC MATERIALS PRINCIPLES AND APPLICATIONS PDF FREE NETWORKS FUZZY LOGIC AND GENETIC ALGORITHMS SYNTHESIS.

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing

Abstract: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples.Key features:Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB exercises and applications in each chapterPresents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systemsConsiders real world problems in the domain of systems modelling, control and optimizationContains a foreword written by Lotfi ZadehComputational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions

Abstract: Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented.

Generalized hesitant fuzzy sets and their application in decision support system

Abstract: Hesitant fuzzy sets are very useful to deal with group decision making problems when experts have a hesitation among several possible memberships for an element to a set. During the evaluating process in practice, however, these possible memberships may be not only crisp values in [0,1], but also interval values. In this study, we extend hesitant fuzzy sets by intuitionistic fuzzy sets and refer to them as generalized hesitant fuzzy sets. Zadeh's fuzzy sets, intuitionistic fuzzy sets and hesitant fuzzy sets are special cases of the new fuzzy sets. We redefine some basic operations of generalized hesitant fuzzy sets, which are consistent with those of hesitant fuzzy sets. Some arithmetic operations and relationships among them are discussed as well. We further introduce the comparison law to distinguish two generalized hesitant fuzzy sets according to score function and consistency function. Besides, the proposed extension principle enables decision makers to employ aggregation operators of intuitionistic fuzzy sets to aggregate a set of generalized hesitant fuzzy sets for decision making. The rationality of applying the proposed techniques is clarified by a practical example. At last, the proposed techniques are devoted to a decision support system.

Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

Abstract: In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.

jFuzzyLogic: a Java Library to Design Fuzzy Logic Controllers According to the Standard for Fuzzy Control Programming

Abstract: Fuzzy Logic Controllers are a specific model of Fuzzy Rule Based Systems suitable for engineering applications for which classic control strategies do not achieve good results or for when it is too difficult to obtain a mathematical model. Recently, the International Electrotechnical Commission has published a standard for fuzzy control programming in part 7 of the IEC 61131 norm in order to offer a well defined common understanding of the basic means with which to integrate fuzzy control applications in control systems. In this paper, we introduce an open source Java library called jFuzzyLogic which offers a fully functional and complete implementation of a fuzzy inference system according to this standard, providing a programming interface and Eclipse plugin to easily write and test code for fuzzy control applications. A case study is given to illustrate the use of jFuzzyLogic.

Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation

Abstract: Wind energy has become a large contender of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, reasonable wind speed is not adequately sustainable everywhere to build an economical wind farm. In wind energy conversion systems, one of the operational problems is the changeability and fluctuation of wind. In most cases, wind speed can vacillate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate optimal power coefficient value of the wind turbines. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). The back propagation learning algorithm is used for training this network. This intelligent controller is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Fuzzy Failure Mode and Effects Analysis Using Fuzzy Evidential Reasoning and Belief Rule-Based Methodology

Abstract: The main objective of this paper is to propose a new risk priority model for prioritizing failures in failure mode and effects analysis (FMEA) on the basis of fuzzy evidential reasoning (FER) and belief rule-based (BRB) methodology. The technique is particularly intended to resolve some of the shortcomings in fuzzy FMEA (i.e., fuzzy rule-based) approaches. In the proposed approach, risk factors like occurrence (O), severity (S), and detection (D), along with their relative importance weights, are described using fuzzy belief structures. The FER approach is used to capture and aggregate the diversified, uncertain assessment information given by the FMEA team members; the BRB methodology is used to model the uncertainty, and nonlinear relationships between risk factors and corresponding risk level; and the inference of the rule-based system is implemented using the weighted average-maximum composition algorithm. The Dempster rule of combination is then used to aggregate all relevant rules for assessing and prioritizing the failure modes that have been identified in FMEA. A case study concerning an ocean going fishing vessel in a marine industry is provided and conducted using the proposed model to illustrate its potential applications and benefits.

Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network

Abstract: This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

A review on the applications of type-2 fuzzy logic in classification and pattern recognition

Abstract: In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic. In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented.

Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

Abstract: In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are being used for fuzzification, artificial neural networks method is being preferred for using in defining fuzzy relationships. In this study, a hybrid fuzzy time series approach is proposed to reach more accurate forecasts. In the proposed hybrid approach, fuzzy c-means clustering method and artificial neural networks are employed for fuzzification and defining fuzzy relationships, respectively. The enrollment data of University of Alabama is forecasted by using both the proposed method and the other fuzzy time series approaches. As a result of comparison, it is seen that the most accurate forecasts are obtained when the proposed hybrid fuzzy time series approach is used.

A Neuro-Fuzzy Approach for Medical Image Fusion

Abstract: This paper addresses a novel approach to the multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of the nonsubsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN). The linking strengths of the RPCNNs' neurons are adaptively set by modeling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of the RPCNN with a less complex structure and having less number of parameters leads to computational efficiency-an important requirement of point-of-care health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details, and unwanted image degradations, etc. Subjective and objective evaluations show better performance of this new approach compared to the existing techniques.

Weighted Fuzzy Spiking Neural P Systems

Abstract: Spiking neural P systems (SN P systems) are a new class of computing models inspired by the neurophysiological behavior of biological spiking neurons. In order to make SN P systems capable of representing and processing fuzzy and uncertain knowledge, we propose a new class of spiking neural P systems in this paper called weighted fuzzy spiking neural P systems (WFSN P systems). New elements, including fuzzy truth value, certain factor, weighted fuzzy logic, output weight, threshold, new firing rule, and two types of neurons, are added to the original definition of SN P systems. This allows WFSN P systems to adequately characterize the features of weighted fuzzy production rules in a fuzzy rule-based system. Furthermore, a weighted fuzzy backward reasoning algorithm, based on WFSN P systems, is developed, which can accomplish dynamic fuzzy reasoning of a rule-based system more flexibly and intelligently. In addition, we compare the proposed WFSN P systems with other knowledge representation methods, such as fuzzy production rule, conceptual graph, and Petri nets, to demonstrate the features and advantages of the proposed techniques.

Fuzzy Rules Interpolation for Sparse Fuzzy Rule-Based Systems Based on Interval Type-2 Gaussian Fuzzy Sets and Genetic Algorithms

Abstract: In this paper, we present a new method for fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 Gaussian fuzzy sets and genetic algorithms. First, we present a method to deal with the interpolation of fuzzy rules based on interval type-2 Gaussian fuzzy sets. We also prove that the proposed method guarantees to produce normal interval type-2 Gaussian fuzzy sets. Then, we present a method to learn optimal interval type-2 Gaussian fuzzy sets for sparse fuzzy rule-based systems based on genetic algorithms. We also apply the proposed fuzzy rules interpolation method and the proposed learning method to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed fuzzy rules interpolation method using the optimally learned interval type-2 Gaussian fuzzy sets gets higher average accuracy rates than the existing methods.

Knowledge-Leverage-Based TSK Fuzzy System Modeling

Abstract: Classical fuzzy system modeling methods consider only the current scene where the training data are assumed to be fully collectable. However, if the data available from the current scene are insufficient, the fuzzy systems trained by using the incomplete datasets will suffer from weak generalization capability for the prediction in the scene. In order to overcome this problem, a knowledge-leverage-based fuzzy system (KL-FS) is studied in this paper from the perspective of transfer learning. The KL-FS intends to not only make full use of the data from the current scene in the learning procedure, but also effectively leverage the existing knowledge from the reference scenes. Specifically, a knowledge-leverage-based Takagi-Sugeno-Kang-type Fuzzy System (KL-TSK-FS) is proposed by integrating the corresponding knowledge-leverage mechanism. The new fuzzy system modeling technique is evaluated through experiments on synthetic and real-world datasets. The results demonstrate that KL-TSK-FS has better performance and adaptability than the traditional fuzzy modeling methods in scenes with insufficient data.

A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

Abstract: The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.

Soft Computing and Human-Centered Machines

Abstract: Introduction multisets and fuzzy multisets model logic, rough sets, and fuzzy sets fuzzy cognitive maps - analysis and extensions methods in hard and fuzzy clustering soft-competitive learning paradigms aggregation operations for fusing fuzzy information fuzzy gated neural networks in pattern recognition soft computing technique in kansei (emotional) information processing vagueness in human judgment and decision making chaos and time series analysis a short course for fuzzy set theory.

Universal Fuzzy Models and Universal Fuzzy Controllers for Stochastic Nonaffine Nonlinear Systems

Abstract: This paper investigates the universal fuzzy model and universal fuzzy controller problems for stochastic nonaffine nonlinear systems. The underlying mechanism of stochastic fuzzy logic is first discussed, and a stochastic generalized fuzzy model with new stochastic fuzzy rule base is then given. Based on their function approximation capability, these kinds of stochastic generalized fuzzy models are shown to be universal fuzzy models for stochastic nonaffine nonlinear systems under some sufficient conditions. An approach to stabilization controller design for stochastic nonaffine nonlinear systems is then developed through their stochastic generalized Takagi-Sugeno (T-S) fuzzy approximation models. Then, the results of universal fuzzy controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy controllers, are also provided, respectively. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach.

Complex Neurofuzzy ARIMA Forecasting—A New Approach Using Complex Fuzzy Sets

Abstract: A novel complex neurofuzzy autoregressive integrated moving average (ARIMA) computing approach is presented for the problem of time-series forecasting. The proposed approach integrates a complex neurofuzzy system (CNFS) using complex fuzzy sets (CFSs) and ARIMA models to form the proposed computing model, which is called the CNFS-ARIMA. The output of CNFS-ARIMA is complex-valued, of which the real and imaginary parts can be used for two different functional mappings. This is the so-called dual-output property. There is no fuzzy If-Then rule in the genesis of CNFS-ARIMA. For the formation of CNFS-ARIMA, structure learning and parameter learning are involved to self-organize and self-tune the CNFS-ARIMA. A class of CFSs is used to describe the premise parts of fuzzy If-Then rules, whose consequent parts are specified by ARIMA models. CFS is an advanced fuzzy set whose membership degrees are complex-valued within the unit disc of the complex plane. With the synergetic merits of CNFS and ARIMA, CNFS-ARIMA models have excellent nonlinear mapping capability for time-series forecasting. A number of benchmark time series are used to test the proposed approach, whose results are compared with those by other approaches. Moreover, real-world financial time series, such as the National Association of Securities Dealers Automated Quotation (NASDAQ), the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), and the Dow Jones Industrial (DJI) Average Index, are used for the proposed approach to perform the dual-output forecasting experiments. The experimental results indicate that the proposed approach shows excellent performance.

Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability

Abstract: Current studies of type-2 neural fuzzy systems (FSs) (NFSs) primarily focus on building a fuzzy model with high accuracy and disregard the interpretability of fuzzy rules. This paper proposes a data-driven interval type-2 (IT2) NFS with improved model interpretability (DIT2NFS-IP). The DIT2NFS-IP uses IT2 fuzzy sets in its antecedent part and intervals in its zero-order Takagi-Sugeno-Kang-type consequent part for rule form simplicity. The initial rule base is generated by a self-splitting clustering algorithm in the input-output space. The DIT2NFS-IP uses a two-phase parameter-learning algorithm to design an accurate model with improved rule interpretability. In the first phase, a new cost function that considers both accuracy and transparent fuzzy set partition is defined. The antecedent and consequent parameters are learned through gradient descent and rule-ordered recursive least squares algorithms, respectively, to achieve cost function minimization. The second phase performs a fuzzy set reduction, followed by consequent parameter learning to improve accuracy. Comparisons with different type-1 and type-2 FSs in five databased modeling and prediction problems verify the performance of the DIT2NFS-IP in both model accuracy and interpretability.

A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

Abstract: Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi–Sugeno–Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules

Abstract: Fuzzy set theory has been used as an approach to deal with uncertainty in the supplier selection decision process. However, most studies limit applications of fuzzy set theory to outranking potential suppliers, not including a qualification stage in the decision process, in which non-compensatory types of decision rules can be used to reduce the set of potential suppliers. This paper presents a supplier selection decision method based on fuzzy inference that integrates both types of approaches: a non-compensatory rule for sorting in qualification stages and a compensatory rule for ranking in the final selection. Fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multicriteria decision making methods. Fuzzy inference combined with a fuzzy rule-based classification method is used to categorize suppliers in qualification stages. Classes of supplier performance can be represented by linguistic terms, which allow decision makers to deal with subjectivity and to express qualification requirements in linguistic formats. Implementation of the proposed method and techniques were analyzed and discussed using an illustrative case. Three defuzzification operators were used in the final selection, yielding the same ranking. Factorial design was applied to test consistency and sensitivity of the inference rules. The findings reinforce the argument that including stages of qualification based on fuzzy inference and categorization makes this method especially useful for selecting from a large set of potential suppliers and also for first time purchase.

High-order fuzzy-neuro expert system for time series forecasting

Abstract: In this article, we present a new model based on hybridization of fuzzy time series theory with artificial neural network (ANN). In fuzzy time series models, lengths of intervals always affect the results of forecasting. So, for creating the effective lengths of intervals of the historical time series data set, a new ''Re-Partitioning Discretization (RPD)'' approach is introduced in the proposed model. Many researchers suggest that high-order fuzzy relationships improve the forecasting accuracy of the models. Therefore, in this study, we use the high-order fuzzy relationships in order to obtain more accurate forecasting results. Most of the fuzzy time series models use the current state's fuzzified values to obtain the forecasting results. The utilization of current state's fuzzified values (right hand side fuzzy relations) for prediction degrades the predictive skill of the fuzzy time series models, because predicted values lie within the sample. Therefore, for advance forecasting of time series, previous state's fuzzified values (left hand side of fuzzy relations) are employed in the proposed model. To defuzzify these fuzzified time series values, an ANN based architecture is developed, and incorporated in the proposed model. The daily temperature data set of Taipei, China is used to evaluate the performance of the model. The proposed model is also validated by forecasting the stock exchange price in advance. The performance of the model is evaluated with various statistical parameters, which signify the efficiency of the model.