Kim-Fung Man

An optimal fuzzy PID controller

This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytical formulae. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization.

Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction

A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic algorithm (MOGA), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can manipulate the parameter genes in a more effective manner. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. Some important concepts about the interpretability are introduced and the fitness function in the MOGA will consider both the accuracy and interpretability of the fuzzy model. In order to remove the redundancy of the rule base proactively, we further apply an interpretability-driven simplification method to newborn individuals. In our approach, we first apply the fuzzy clustering to generate an initial rule-based model. Then the multi-objective hierarchical genetic algorithm and the recursive least square method are used to obtain the optimized fuzzy models. The accuracy and the interpretability of fuzzy models derived by this approach are studied and presented in this paper. We compare our work with other methods reported in the literature on four examples: a synthetic nonlinear dynamic system, a nonlinear static system, the Lorenz system and the Mackey-Glass system. Simulation results show that the proposed approach is effective and practical in knowledge extraction.

Genetic algorithms for control and signal processing

The practical application of genetic algorithms (GA) to the solution of engineering problems is a rapidly emerging approach in the field of control engineering and signal processing. This tutorial provides a comprehensive coverage of the techniques involved, describing the characteristics, advantages and constraints of GA, as well as discussing genetic operations such as crossover, mutation and reinsertion. The intrinsic characteristics in term parallelism, multiobjective, and multimodal etc. are outlined. The features of this approach are illustrated by real-world applications. Also described is a newly proposed and unique hierarchical genetic algorithm designed to address the problem in determining system topology.

Minimal fuzzy memberships and rules using hierarchical genetic algorithms

A new scheme to obtain optimal fuzzy subsets and rules is proposed. The method is derived from the use of genetic algorithms, where the genes of the chromosome are classified into two different types. These genes can be arranged in a hierarchical form, where one type of gene controls the other. The effectiveness of this genetic formulation enables the fuzzy subsets and rules to be optimally reduced and, yet, the system performance is well maintained. In this paper, the details of formulation of the genetic structure are given. The required procedures for coding the fuzzy membership function and rules into the chromosome are also described. To justify this approach to fuzzy logic design, the proposed scheme is applied to control a constant water pressure pumping system. The obtained results, as well as the associated final fuzzy subsets, are included in this paper. Because of its simplicity, the method could lead to a potentially low-cost fuzzy logic implementation.

Design and optimization of IIR filter structure using hierarchical genetic algorithms

A new genetic algorithm (GA) is proposed for digital filter design. This scheme utilizes a new hierarchical multilayer gene structure for the chromosome formulation. This is a unique structure, which retains the conventional genetic operations, while the genes may take various forms to represent the system characteristics. As a result, both the system structure and the parametric variables can be optimized in a simultaneous manner, without extra computational cost and effort. It has been demonstrated that this technique not only fulfils all types of filter performance requirements, but that the lowest order of the filter can also be found.

A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization

Abstract This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749–768; T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber repeater placement using a jumping gene algorithm, in: Proceedings of the IEEE International Conference on Industrial Technology (ICIT 2005), Hong Kong, 2005, pp. 291–296; K.F. Man, T.M. Chan, K.S. Tang, S. Kwong, Jumping-genes in evolutionary computing, in: Proceedings of the IEEE IECON’2004, Busan, 2004, pp. 1268–1272]. JGGA is a relatively new multiobjective evolutionary algorithm (MOEA) that imitates a jumping gene phenomenon discovered by Nobel Laureate McClintock during her work on the corn plants. The main feature of JGGA is that it only has a simple operation in which a transposition of gene(s) is induced within the same or another chromosome in the genetic algorithm (GA) framework. In its initial formulation, the search space solutions are binary-coded and it inherits the customary problems of conventional binary-coded GA (BCGA). This issue motivated us to remodel the JGGA into RJGGA. The performance of RJGGA has been compared to other MOEAs using some carefully chosen benchmark test functions. It has been observed that RJGGA is able to generate non-dominated solutions with a wider spread along the Pareto-optimal front and better address the issues regarding convergence and diversity in multiobjective optimization.

A Jumping Gene Paradigm for Evolutionary Multiobjective Optimization

A new evolutionary computing algorithm on the basis of the ldquojumping genesrdquo (JG) phenomenon is proposed in this paper. It emulates the gene transposition in the genome that was discovered by Nobel Laureate, Barbara McClintock, in her work on the corn plants. The principle of JGs that is adopted for evolutionary computing is outlined. The procedures for executing the computational optimization are provided. A large number of constrained and unconstrained test functions have been utilized to verify this new scheme. Its performances on convergence and diversity have been statistically examined and comparisons with other evolutionary algorithms are carried out. It has been discovered that this new scheme is robust and able to provide outcomes quickly and accurately. A stringent measure of binary-indicator is also applied for algorithm classification. The outcome from this test indicates that the JG paradigm is a very competitive scheme for multiobjective optimization and also a compatible evolutionary computing scheme when speed in convergence, diversity, and accuracy are simultaneously required.

Agent-based evolutionary approach for interpretable rule-based knowledge extraction

An agent-based evolutionary approach is proposed to extract interpretable rule-based knowledge. In the multiagent system, each fuzzy set agent autonomously determines its own fuzzy sets information, such as the number and distribution of the fuzzy sets. It can further consider the interpretability of fuzzy systems with the aid of hierarchical chromosome formulation and interpretability-based regulation method. Based on the obtained fuzzy sets, the Pittsburgh-style approach is applied to extract fuzzy rules that take both the accuracy and interpretability of fuzzy systems into consideration. In addition, the fuzzy set agents can cooperate with each other to exchange their fuzzy sets information and generate offspring agents. The parent agents and their offspring compete with each other through the arbitrator agent based on the criteria associated with the accuracy and interpretability to allow them to remain competitive enough to move into the next population. The performance with emphasis upon both the accuracy and interpretability based on the agent-based evolutionary approach is studied through some benchmark problems reported in the literature. Simulation results show that the proposed approach can achieve a good tradeoff between the accuracy and interpretability of fuzzy systems.

A systematic approach to generating n-scroll attractors

A new circuitry design based on Chua’s circuit for generating n-scroll attractors (n =1 ; 2; 3;:::) is proposed. In this design, the nonlinear resistor in Chua’s circuit is constructed via a systematical procedure using basic building blocks. With the proposed construction scheme, the slopes and break points of the v{i characteristic of the circuit can be tuned independently, and chaotic attractors with an even or an odd number of scrolls can be easily generated. Distinct attractors with n-scrolls (n =5 ; 6; 7; 8; 9; 10) obtained with this simple experimental set-up are demonstrated.

Using genetic algorithms to design mesh networks

Designs for mesh communication networks must meet conflicting, interdependent requirements. This sets the stage for a complex problem with a solution that targets optimal topological connections, routing, and link capacity assignments. These assignments must minimize cost while satisfying traffic requirements and keeping network delays within permissible values. Since such a problem is NP-complete, developers must use heuristic techniques to handle the complexity and solve practical problems with a modest number of nodes. One heuristic technique, genetic algorithms, appears to be ideal to handle the design of mesh networks with capability of handling discrete values, multiobjective functions, and multiconstraint problems. Existing applications of genetic algorithms to this problem, however, have only optimized the network topology. They ignore the difficult subproblems of routing and capacity assignment, a crucial determiner of network quality and cost. This article presents a total solution to mesh network design using a genetic algorithm approach. The application is a 10-city network that links Hong Kong and nine other cities in China. The development demonstrates that this method can be used for networks of reasonable size with realistic topology and traffic requirements.