Ching-Chang Wong

DESIGN OF FUZZY SYSTEMS WITH FEWER RULES

This article presents an innovative method for designing fuzzy systems composed of fewer fuzzy rules. The conventional approach to fuzzy-system design usually assumes that there exists no correlation among input variables, therefore, grid-type fuzzy partitions are widely adopted. However, in many cases, it is likely that input variables are highly correlated with one another. To avoid the problem of growth of partitioned grids in some complex system, we used an aggregation of hyperrectangulars with different size and different positions to approximate fuzzy partitions that are arbitrarily shaped. The corresponding parameters defining these hyperrectangulars are selected by using genetic algorithms. Furthermore, the number of fuzzy rules of the constructed system can be automatically minimized by choosing a special fitness function that takes this factor into account. Finally, an inverted pendulum control and nonlinear modeling problems are utilized to illustrate the effectiveness of the proposed method.

A on-line rule tuning grey prediction fuzzy control system design

An on-line rule tuning grey prediction fuzzy control system is presented, which contains the advantages of grey prediction, fuzzy theory and the on-line tuning algorithm. The on-line rule tuning grey prediction fuzzy control system structure is constructed so that the rise time and the overshoot of the controlled system can be maintained simultaneously.

FUZZY SYSTEM DESIGN BY A GA-BASED METHOD FOR DATA CLASSIFICATION

In this paper, a method based on the Genetic Algorithm (GA) and SVD-QR method is proposed to construct an appropriate fuzzy system for data classification. In this method, an individual of the population in the GA is used to determine a fuzzy partition such that some rough fuzzy sets of each input variable are obtained. In order to extract significant fuzzy rules from the rule base of the defined fuzzy system, the SVD-QR method is applied to remove unnecessary fuzzy rules such that the constructed fuzzy system has a low number of fuzzy rules. A fitness function in the GA is considered to guide the search procedure to select an appropriate fuzzy system such that the number of correctly classified patterns are maximized and the number of fuzzy rules is minimized. Finally, a classification problem is considered to illustrate the effectiveness of the proposed method.

MULTITUNING FUZZY CONTROL SYSTEMS DESIGN

A multituning fuzzy control system structure that involves two simple, but effective tuning mechanisms, is proposed: one is called fuzzy control rule tuning mechanism (FCRTM); the other is called dynamic scalar tuning mechanism (DSTM). In FCRTM, it is used to generate the necessary control rules with a center extension method. In DSTM, it contains three fuzzy IF-THEN rules for determining the appropriate scaling factors for the fuzzy control system. In this paper, a method based on the genetic algorithm (GA) is proposed to simultaneously choose the appropriate parameters in FCRTM and DSTM. That is, the proposed GA-based method can automatically generate the required rule base of fuzzy controller and efficiently determine the appropriate map for building the dynamic scalars of fuzzy controller. A multiobjective fitness function is proposed to determine an appropriate parameter set such that not only the selected fuzzy control structure has fewer fuzzy rules, but also the controlled system has a good control performance. Finally, an inverted pendulum control problem is given to illustrate the effectiveness of the proposed control scheme.A multituning fuzzy control system structure that involves two simple, but effective tuning mechanisms, is proposed: one is called fuzzy control rule tuning mechanism (FCRTM); the other is called dynamic scalar tuning mechanism (DSTM). In FCRTM, it is used to generate the necessary control rules with a center extension method. In DSTM, it contains three fuzzy IF-THEN rules for determining the appropriate scaling factors for the fuzzy control system. In this paper, a method based on the genetic algorithm (GA) is proposed to simultaneously choose the appropriate parameters in FCRTM and DSTM. That is, the proposed GA-based method can automatically generate the required rule base of fuzzy controller and efficiently determine the appropriate map for building the dynamic scalars of fuzzy controller. A multiobjective fitness function is proposed to determine an appropriate parameter set such that not only the selected fuzzy control structure has fewer fuzzy rules, but also the controlled system has a good contro...

Fewer Hyper-Ellipsoids Fuzzy Rules Generation Using Evolutional Learning Scheme

Fuzzy rules generation is known an important task in designing fuzzy systems. This article applies an evolutionary fuzzy rules learning scheme to approach desired fuzzy systems having a lower fuzzy rules. The proposed learning scheme overcomes limitations of conventional fuzzy rules generation and completes the complex searching problems to extract the desired fuzzy system. In this article, aggregations of hyper-ellipsoids fuzzy partitions with different sizes and different positions are suggested to approximate the knowledge rule base of fuzzy systems whose membership functions are arbitrarily shaped and flexibly tuned in parameters searching space. Several corresponding parameters in defining the region of such hyper-ellipsoids type membership functions are efficiently selected based on the simple rule extracting technology. Furthermore, the constructed fuzzy system with only two fuzzy rules can be automatically extracted by the evolutional genetic algorithms (GAs) learning scheme with the guide of special fitness function. Finally, both inverted pendulum balance and nonlinear modeling problems are used to illustrate the effectiveness of the proposed method.

Design and Implementation of GA-based Fuzzy System on FPGA CHIP

In this article, a design and implementation methodology of a GA-based fuzzy system on a Field Programmable Gate Array (FPGA) chip is proposed. First, a self-generating method based on a genetic algorithm (GA) is proposed to automatically construct a high performance fuzzy system. Some simulation results of an inverted pendulum control system are presented to illustrate the efficiency of the proposed GA-based method in the fuzzy system design. Then, a fuzzy system design method by using VHSIC Hardware Description Language (VHDL) is proposed so that the fuzzy system hardware implemented on an FPGA chip has a great flexibility and a high process speed. Moreover, a friendly-used software tool is developed to automatically generate VHDL codes for the users to easily design their fuzzy system hardware based on the proposed methodology. Finally, some experimental results are presented to illustrate the validity and the applicability of the design methodology.

SIGNIFICANT FUZZY RULES EXTRACTION BY AN SVD–QR-BASED APPROACH

ABSTRACT In this article, an SVD–QR-based approach is proposed to extract the important fuzzy rules from a rule base with several fuzzy rule tables to design an appropriate fuzzy system directly from some input-output data of the identified system. A fuzzy system with fuzzy rule tables is defined to approach the input-output pairs of an identified system. In the rule base of the defined fuzzy system, each fuzzy rule table corresponds to a partition of an input space. In order to extract the important fuzzy rules from the rule base of the defined fuzzy system, a firing strength matrix determined by the membership functions of the premise fuzzy sets is constructed. According to the firing strength matrix, the number of important fuzzy rules is determined by the Singular Value Decomposition SVD, and the important fuzzy rules are selected by the SVD–QR-based method. Consequently, a reconstructed fuzzy rule base composed of significant fuzzy rules is determined by the firing strength matrix. Furthermore, the recursive least-squares method is applied to determine the consequent part of the reconstructed fuzzy system according to the gathered input-output data so that a fine fuzzy system is determined by the proposed method. Finally, three nonlinear systems illustrate the efficiency of the proposed method.

Evolutional RBFNs image model describing-based segmentation system designs

Abstract Knowledge discovered-based radial basis function neural networks (RBFNs) model can describe an appropriate behaviors of identified image patterns through the multiple and hybrid learning schemes. The image data extraction learning algorithm (IDELA) with dynamic recognitions to automatically match the appropriate feature with a suitable number of radial basis function (RBFs). This first step approaches their associated centers positions to extract initial prototypes. The approximated image model as a describer is automatically generated by the RBFPSO learning scheme, which is contained hybrid bacterial foraging particle swarm optimization (BFPSO) algorithm and recursive least-squares (RLS) iterations to deeply approach the image feature. Due to the limitations and possible local learning trap, K-means, differential evolution (DE) and particle swarm optimization (PSO) learning algorithms cannot obtain the most smaller Root-Mean-Square Error (RMSE) to achieve an appropriate image segmentation in all experiment cases. The constructed RBFNs image model is generated by the support of multiple image self-extraction feature machine (MISEFM), which combined IDELA and RBFPSO algorithms to develop the universal RBFNs image describers. Simulations compared with other K-means, PSO and DE learning methods, show the average great performance in several real image segmentation applications. The peak signal-to-noise ratio (PSNR) index is selected to evaluate the quality of the reconstructed images. Simulations show that the evolutional hybrid and multi-level RBFNs image model-based system is determined to simultaneously achieve both high performance indexes on accuracy (RMSE) and a high image quality description (PSNR) for matching the desired characters and behaviors of image patterns within a fewer RBFs functions.

Evolutional-based network traffic flow behavior acquisition and fuzzy control system design

A theory of IEEE 802.11e of the wireless channel service concept is applied in this paper. The integration of particle swarm optimization (PSO), fuzzy inference theory and Markov Chains model are proposed to design the evolutionary-based network traffic flow behavior acquisition and fuzzy control system. Markov Chains model simulates the flow saturation state of internal channel competitions to detect the channel busy probability with the regulation of backoff cycles. Wireless network flow model is developed to apply time serious service package based on the distribution of collided flow probability. The acquisition features of network traffic flow describe the package sending behavior to construct the fuzzy control architecture. Suitable fuzzy rules are selected by the PSO learning scheme to improve the flow congestion condition and shorten the service waiting time. Computer simulation results on two network station problems are derived to demonstrate the efficiency of the proposed methods.

A multiple tuning fuzzy control system design

A multiple tuning method is proposed to develop fuzzy control system such that the output of the controlled system has the desired output without knowing the mathematical model of the controlled system. In this control structure, a multiple tuning algorithm based on the technology of reinforcement learning and decision making algorithm is constructed to enable it to tune the consequent parameters of the fuzzy controller such that the fuzzy controller has the self-tuning ability. In this multiple tuning method, a state evaluator is considered to play the role of a critic element to evaluate the current state of the controlled system. A functional-type evaluator is used to produce a scalar value, which is provided to a parameter modifier to tune the adjustable parameters of the fuzzy controller. A decision making algorithm work as a selector to select a appropriate parameter and fed a better action to the plant such that the controlled system has a better performance. The goal of the multiple tuning algorithm is to maximize the evaluation value of the current state such that the control objective can be attained. Finally, the inverted pendulum control problem is used to illustrate the effectiveness of the proposed control system structure.