Kit-Sang Tang

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.

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 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.

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.

Optimisation of HMM topology and its model parameters by genetic algorithms

Abstract Hidden Markov model (HMM) is currently the most popular approach to speech recognition. However, the problems of finding a good HMM model and its optimised model parameters are still of great interest to the researchers in this area. In our previous work, we have successfully applied the genetic algorithm (GA) to the HMM training process to obtain the optimised model parameters (Chau et al. Proc. ICASSP (1997) 1727) of the HMM models. In this paper, we further extend our work and propose a new training method based on GA and Baum–Welch algorithms to obtain an HMM model with optimised number of states in the HMM models and its model parameters. In this work, we are not only able to overcome the shortcomings of the slow convergence speed of the simple GA-HMM approach. In addition, this method also finds better number of states in the HMM topology as well as its model parameters. From our experiments with the 100 words extracted from the TIMIT corpus, our method is able to find the optimal topology in all cases. In addition, the HMMs trained by our GA HMM training have a better recognition capability than the HMMs trained by the Baum–Welch algorithm. In addition, 290 words are randomly selected from the TMIIT database for testing the recognition performances of both approaches, it is found that the GA-HMM approach has a recognition rate of 95.86% while the Baum–Welch method has a recognition rate of 93.1%. This implies that the HMMs trained by our GA-HMM method are more optimised than the HMMs trained by the Baum–Welch method.

Generating chaos via x|x|

This paper investigates the role of the function x|x| as a chaos generator in nonautonomous systems. A Duffing-like nonautonomous oscillator is used for illustration. It is rigorously proven via the Melnikov function method that this particular quadratic function induces Smale horseshoes to the Duffing-like system. Moreover, its physical meaning as an energy function is demonstrated, which provides a critical value for the emergence of chaos. Simulations with bifurcation analysis are given for better understanding of the underlying dynamics.

Generating chaos in Chua's circuit via time-delay feedback

A time-delay chaotification approach can be applied to the Chuas circuit by adding a small-amplitude time-delay feedback voltage to the circuit. The chaotic dynamics of this newly derived time-delay Chuas circuit is studied by theoretical analysis, verified by computer simulations as well as by circuit experiments.

Wireless communication network design in IC factory

A wireless local area network (WLAN) is designed for an IC factory in Hong Kong using the hierarchical genetic algorithm (HGA). The HGA is capable of handling multiobjective functions and discrete constraints. Because of this uniqueness, together with the adoption of a Pareto ranking scheme, a solution can be reached even when skewed multiobjective functions and constraints confinements are being imposed. It has been found from this study that a precise number of base stations can be identified for the WLAN network, while it can satisfy a number of objectives and constraints. This added feature provides a further design tradeoff between cost and performance at no extra effort.