F H F Leung

Quality and robustness improvement for real world industrial systems using a fuzzy particle swarm optimization

This paper presents a novel fuzzy particle swarm optimization with cross-mutated (FPSOCM) operation, where a fuzzy logic system developed based on the knowledge of swarm intelligence is proposed to determine the inertia weight for the swarm movement of particle swarm optimization (PSO) and the control parameter of a newly introduced cross-mutated operation. Hence, the inertia weight of the PSO can be adaptive with respect to the search progress. The new cross-mutated operation intends to drive the solution to escape from local optima. A suite of benchmark test functions are employed to evaluate the performance of the proposed FPSOCM. Experimental results show empirically that the FPSOCM performs better than the existing hybrid PSO methods in terms of solution quality, robustness, and convergence rate. The proposed FPSOCM is evaluated by improving the quality and robustness of two real world industrial systems namely economic load dispatch system and self-provisioning systems for communication network services. These two systems are employed to evaluate the effectiveness of the proposed FPSOCM as they are multi-optima and non-convex problems. The performance of FPSOCM is found to be significantly better than that of the existing hybrid PSO methods in a statistical sense. These results demonstrate that the proposed FPSOCM is a good candidate for solving product or service engineering problems which have multi-optima or non-convex natures.

An improved stability analysis and design of fuzzy control systems

This paper presents the stability analysis and design of fuzzy control systems. An improved and simple stability condition will be derived based on the Lyapunovs stability theory. The derived stability condition involves a smaller number of Lyapunovs conditions than that given by Wang et al. (1996). A design of the membership functions of the fuzzy controller will be given. In our approach, the number of rules of the fuzzy controller can be different from that of the fuzzy plant model. Our approach can be applied to those fuzzy controllers with both positive and negative grades of membership, An application example on stabilizing a mass-spring-damper system will be given to show the stabilizability of the fuzzy controller.

Fuzzy Control of DC-DC Switching Converters: Stability and Robustness Analysis

Abstract This paper presents the design of a fuzzy controller for PWM (pulse width modulation) DC-DC switching converter based on the Takagi-Sugeno (TS) fuzzy modelling approach. Stability and robustness conditions are derived for the fuzzy control system to help the design of the fuzzy controlled DC-DC converter. Simulation and experimental results on regulating a boost DC-DC converter subject to large load changes by using the proposed fuzzy controller are given. The transient response is compared to that controlled by a traditional PI controller.

Restoration of half-toned color-quantized images using Particle Swarm Optimization with wavelet mutation

Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with halftoning. Most existing restoration algorithms are generally inadequate to deal with this problem as they were proposed for restoring noisy blurred images. In this paper, a restoration algorithm based on particle swarm optimization with wavelet mutation (WPSO) is proposed to solve the problem. This algorithm makes a good use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results show that it can improve the quality of a half-toned color-quantized image remarkably in terms of both SNRI and convergence rate. The subjective quality of the restored images can also be improved.

Hypoglycemia detection using fuzzy inference system with genetic algorithm

In this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the electrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.

Fuzzy-model-based Control Systems Using Fuzzy Combination Techniques

This paper presents a fuzzy combined model and a fuzzy combined controller to handle nonlinear systems. A fuzzy combination of some local fuzzy models is employed to represent a nonlinear system. Based on this fuzzy combined model, a fuzzy controller combining some local fuzzy controllers is proposed to control the nonlinear system. Conditions are derived to guarantee the system stability. By using the fuzzy combination technique, the stability analysis is reduced to investigating only the stability of the local fuzzy control systems. The system that combines the local fuzzy systems will then be guaranteed stable. This property reduces the difficulty of finding the solution to the stability conditions even though the fuzzy combined model has effectively distributed the complex nonlinearity over the local fuzzy models. Furthermore, any sharp changes in the system states and control signals caused by the switching activities between local fuzzy control systems are smoothed out by the fuzzy combined controller. An application example will be given to show the merits of the proposed approach.