Design Of Fuzzy System Research Articles

A hybrid of electromagnetism-like mechanism and back-propagation algorithms for recurrent neural fuzzy systems design

This article introduces a novel hybrid evolutionary algorithm for recurrent fuzzy neural systems design in applications of nonlinear systems. The hybrid learning algorithm, IEMBP-improved electromagnetism-like (EM) with back-propagation (BP) technique, combines the advantages of EM and BP algorithms which provides high-speed convergence, higher accuracy and less computational complexity (computation time in seconds). In addition, the IEMBP needs only a small population to outperform the standard EM that uses a larger population. For a recurrent neural fuzzy system, IEMBP simulates the ‘attraction’ and ‘repulsion’ of charged particles by considering each neural system parameters as a charged particle. The EM algorithm is modified in such a way that the competition selection is adopted and the random neighbourhood local search is replaced by BP without evaluations. Thus, the IEMBP algorithm combines the advantages of multi-point search, global optimisation and faster convergence. Finally, several illustration examples for nonlinear systems are shown to demonstrate the performance and effectiveness of IEMBP.

A simplified structure evolving method for Mamdani fuzzy system identification and its application to high-dimensional problems

This paper proposes the Simplified Structure Evolving Method (SSEM) for fuzzy system identification, which improves our earlier work on the Structure Evolving Learning Method for fuzzy systems (SELM). The improvement is that SSEM applies a scheme that starts with the simplest fuzzy rule set with only one fuzzy rule (instead of 2n fuzzy rules as in SELM, where n is the number of input variables), whilst retaining all the advantages of SELM. SELM is able to solve the problem of the exponential increase of fuzzy rules, however, it requires a basic fuzzy rule set which is exponential to the number of input variables (2n fuzzy rules) as a starting point. The improvement offered by SSEM enables automatic feature selection and system structure identification, and avoids inefficient rules and inefficient variable involvement for system identification. This improvement enables fuzzy systems to be applicable to problems of any input dimension. Three benchmark examples with high dimension inputs are given to illustrate the advantages of the proposed algorithm.

Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System

This paper presents the design and application of fuzzy neural network control system for the rotary cement kiln system. Due to the dynamic characteristics and reaction process parameters are with features of large inertia, pure hysteresis, nonlinearity and strong coupling, the fuzzy neural network controller combining both the advantages of neural network and fuzzy control was applied. The fuzzy neural network is an adaptive control process whose parameters can be adjusted by learning algorithms automatically which is aimed to eliminate the shortcomings of conventional control methods. The main control system structure includes mainly the pressure control loop, the burning zone control loop and the back-end of kiln temperature control loop. Simulation results show the effectiveness of the control scheme with satisfied dynamic performances on response time and overshoot.

Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System

This paper presents the design and application of fuzzy neural network control system for the rotary cement kiln system. Due to the dynamic characteristics and reaction process parameters are with features of large inertia, pure hysteresis, nonlinearity and strong coupling, the fuzzy neural network controller combining both the advantages of neural network and fuzzy control was applied. The fuzzy neural network is an adaptive control process whose parameters can be adjusted by learning algorithms automatically which is aimed to eliminate the shortcomings of conventional control methods. The main control system structure includes mainly the pressure control loop, the burning zone control loop and the back-end of kiln temperature control loop. Simulation results show the effectiveness of the control scheme with satisfied dynamic performances on response time and overshoot.

A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

Abstract In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strategy. A state-of-the-art evolutionary multiobjective algorithm, namely NSGA-II, is used to provide a component classifier selection and improve FURIA-based fuzzy MCS. We propose five different fitness functions based on three different optimization criteria, accuracy, complexity, and diversity. Twenty UCI high dimensional datasets were considered in order to conduct the experiments. A combination between accuracy and diversity criteria provided very promising results, becoming competitive with classical MCS learning methods.

A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling

Abstract In this paper, a clustering-based algorithm is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed clustering method can automatically yield the number of clusters and its associated cluster centers from the input training data. While the features of training data are extracted by the proposed clustering method, the valuable information on the initial structure of the Sugeno-type fuzzy inference system is built up. For testing the performance of the proposed system modeling method, two wellknow examples from the literature and one real-world data set from the Taiwan's stock market are used to illustrate the validity of the proposed fuzzy system design procedure.

An Output-Constrained Clustering Approach for the Identification of Fuzzy Systems and Fuzzy Granular Systems

This paper presents an output-constrained clustering approach for fuzzy system identification and fuzzy granular system identification. The approach is unlike most existing clustering algorithms for structure identification of fuzzy systems, where the focus is on input or combined input-output clustering. The output-constrained clustering algorithm divides the output space into several partitions and each output partition is considered to be a constraint; then, input data are projected into clusters that are based on the input distribution constrained by the output partitions. By introducing the key concept of separability of a set of clusters within each output constraint, the proposed approach automatically finds an appropriate small and efficient number of clusters for each output constraint. To have an appropriate small and efficient number of clusters in each output constraint results in a more compact final system structure and better accuracy. This better performance is illustrated by experiments using benchmark function approximation and dynamic system identification.

Identification of Fuzzy Inference Systems Using a Multi-objective Space Search Algorithm and Information Granulation

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some conventional fuzzy models encountered in the literature.

SaFIN: A Self-Adaptive Fuzzy Inference Network

There are generally two approaches to the design of a neural fuzzy system: 1) design by human experts, and 2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: 1) an inconsistent rulebase; 2) the need for prior knowledge such as the number of clusters to be computed; 3) heuristically designed knowledge acquisition methodologies; and 4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved.

A New Adaptive Fuzzy Sliding Mode Control for Uncertain Nonlinear Systems

A novel Adaptive Fuzzy Sliding Mode Control (AFSMC) methodology is proposed in this paper based on the integration of Sliding Mode Control (SMC) and Adaptive Fuzzy Control (AFC).To get rid of the chattering and the bound of uncertainty, an adaptive fuzzy logic system design method introduced for the switching gain is proposed. The main advantage of our proposed methodology is that the nonlinear systems are unknown and no knowledge of the bounds of parameters, uncertainties and external disturbance are not required in advance.the design for the switching gain which will relax the requirement for the bound of uncertainty can ensure stability. The simulation results illustrate the effectiveness of the method.

Circuit implementation of a fully programmable and continuously slope tunable triangular/trapezoidal membership function generator

In this article, a fully programmable membership function generator (MFG) is proposed. This MFG is capable of generating triangular, trapezoidal as well as both S-shaped and Z-shaped membership functions simultaneously. Utilizing a differential pair as an analog switch leads to relax the design of fuzzy systems control part. This MFG has the ability of adapting itself with various fuzzy controllers which produce different control voltage ranges. Unlike the available reported literatures, this MFG uses a new analog programmable current mirror (APCM) instead of digitally programmable current mirrors to adjust the slopes of membership functions. Extensive time domain simulations have been carried out using Hspice by level 49 parameters (BSIM3v3) in standard CMOS technology to validate the effective performance of the proposed MFG.

Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.

ON DESIGNING FUZZY RULE-BASED MULTICLASSIFICATION SYSTEMS BY COMBINING FURIA WITH BAGGING AND FEATURE SELECTION

In this work, we conduct a study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging and feature selection. We develop an exhaustive study on the potential of bagging and feature selection to design a final FURIA-based fuzzy multiclassifier dealing with high dimensional data. Several parameter settings for the global approach are tested when applied to twenty one popular UCI datasets. The results obtained show that FURIA-based fuzzy multiclassifiers outperform the single FURIA classifier and are competitive with C4.5 multiclassifiers and random forests.

New methods for the estimation of Takagi–Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

SUMMARYThis paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T‐S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T‐S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T‐S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T‐S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T‐S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T‐S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use. Copyright © 2011 John Wiley & Sons, Ltd.

An ensemble of logical-type neuro-fuzzy systems

Neuro-fuzzy classifiers are characterized by incorporation of the expert knowledge into their construction. The most popular neuro-fuzzy systems are Mamdani-type systems. The main groups of neuro-fuzzy systems are also Takagi–Sugeno and logical-type systems. The latter were very rarely studied in the literature, however it was shown that logical-type reasoning transpired to be better suited for classification tasks whereas Mamdani-type reasoning for approximation problems (Rutkowska & Nowicki, 2000; Rutkowski & Cpałka, 2003, 2005). It is well known that an ensemble of several classifiers improves classification accuracy. Many ensembling methods are meta-learning techniques, thus they can be used to design ensembles of various member classifiers.In the paper an ensemble of logical-type neuro-fuzzy systems, based on S-implications, is proposed. The design of such an ensemble, and fuzzy ensemble in general, is a challenging task and encounters technical difficulties. The major problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. Several experiments illustrate the idea presented in the paper.

Robust anti-windup controller design of time-delay fuzzy systems with actuator saturations

This study presents a robust anti-windup fuzzy control approach for uncertain nonlinear time-delay systems with actuator saturations. The discussed system dynamics is presented by the Takagi–Sugeno (T–S) fuzzy model. To facilitate the design process, the nonlinearity of input saturation is characterized by a specific sector condition. Given a stabilizing dynamic output feedback fuzzy controller, an anti-windup control approach is then developed to maximize the size of estimate of the domain of attraction. Significantly, the convergence of all admissible initial states within this region could be ensured. Based on the Lyapunov–Krasovskii delay-independent and delay-dependent analyses, sufficient conditions for the robust stabilization are derived. These conditions are formulated as a convex optimization problem with constraints represented by a set of linear matrix inequalities, allowing the fuzzy controller to be synthesized more efficiently. Finally, numeric simulations are given to validate the proposed approach.

Exponential stability analysis and controller design of fuzzy systems with time-delay

Abstract Takagi–Sugeno (T–S) fuzzy models can provide an effective representation of complex nonlinear systems with a series of linear input/output submodels in terms of fuzzy sets and fuzzy reasoning. In this paper, the T–S fuzzy model approach is extended to the stability analysis and controller design for nonlinear systems with time delays. An improved stability condition is proposed by introducing adjustable parameters into the Lyapunov–Krasovskii functional. Stabilization approach for fuzzy state feedback is also presented. Sufficient conditions for the existence of fuzzy feedback gain are derived through the numerical solution of a set of obtained linear matrix inequalities (LMIs). Compared with the existing methods in the literature, the proposed approach has less conservatism and both the sizes of delay and its derivative are involved in the criterion. The dynamical performance of the system can be adjusted by changing the adjustable parameters. Finally, two examples are given to show the effectiveness of the proposed approach.

Designing and Modeling Fuzzy Control Systems

Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (precision) and linguistic representation (interpretability). Fuzzy modeling—meaning the construction of fuzzy systems—is an arduous task, demanding the identification of many parameters. This paper analyses the fuzzy-modeling problem and different approaches to coping with it, focusing on evolutionary fuzzy modeling— the design of fuzzy inference systems using evolutionary algorithms. The purpose of this paper is twofold. We first provide an overview of the standard approach to constructing a fuzzy control system and then identify a wide variety of relevant system modeling techniques. The later part of the paper deals with discussing Fuzzy modeling problem – curse of dimensionality and techniques to solve the problem. The paper provides an introduction to the use of fuzzy sets and fuzzy logic for the approximation of functions and modeling of static and dynamic systems. The concept of a fuzzy system is first explained. Afterwards, the motivation and practical relevance of fuzzy modeling are highlighted.

Computational intelligence software for interval type‐2 fuzzy logic

AbstractA software tool for interval type‐2 fuzzy logic is presented in this article. The software tool includes a graphical user interface for construction, edition, and observation of the fuzzy systems. The Interval Type‐2 Fuzzy Logic System Toolbox (IT2FLS) has a user‐friendly environment for interval type‐2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, are presented as part of the Toolbox. The Toolbox's best properties are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with type‐2 fuzzy operators, linguistic variables, interval type‐2 membership functions, defuzzification methods, and the evaluation of interval type‐2 fuzzy inference systems. The toolbox can be used for educational and research purposes. © 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 737–747, 2013

Design of genetic fuzzy system for forward and reverse mapping of green sand mould system

A genetic fuzzy system has been developed to solve the forward and reverse mapping problems of green sand mould systems. The performance of a fuzzy logic (FL) system depends on its knowledge base, which consists of a database and a rule base. A binary coded genetic algorithm (GA) has been used to optimise the knowledge base for the FL based approaches. Two approaches have been developed for each model (i.e. forward and reverse modelling). In the first approach, a manually compiled database and rule base of the FL system are optimised by GA, whereas in the second approach, the GA is used to evolve the optimal FL system automatically. The membership function distributions of the FL system are assumed to be asymmetric triangular. The first approach is found to perform better than the latter in terms of accuracy in prediction of the responses.