Author
M. Bin Khalid
Bio: M. Bin Khalid is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Fuzzy logic & Adaptive control. The author has an hindex of 2, co-authored 2 publications receiving 255 citations.
Papers
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01 Apr 1999
TL;DR: This paper presents a neuro-fuzzy logic controller where all of its parameters can be tuned simultaneously by GA, and shows that the proposed controller offers encouraging advantages and has better performance.
Abstract: Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.
256 citations
30 Nov 2008
TL;DR: Experimental results showed that the proposed multi-level fuzzy score fusion method improves the performance of the CSLDA based face authentication system compared to the other fusion techniques examined in this work.
Abstract: A client specific linear discriminant analysis (CSLDA) based face authentication system has been developed with multi-level fuzzy score fusion. The CSLDA method provides two measures for authentication: distance to the client (Client Score) and distance to the mean of impostors (Impostor Score). A two-level multi-sample score fusion method has been proposed. A fuzzy inference module has also been developed to combine the scores of the CSLDA in the first level. The performance of fuzzy inference score fusion is then compared with several existing fusion methods and the conventional CSLDA face authentication system (without score fusion). Overall, the proposed fusion methods improve the performance of the algorithm and are more robust to variability of the inputs. Evaluation experiments were carried out with two different databases (AT&T and BANCA) where each contains face images of 40 subjects. Experimental results showed that the proposed multi-level fuzzy score fusion method improves the performance of the CSLDA based face authentication system compared to the other fusion techniques examined in this work.
4 citations
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01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.
2,933 citations
TL;DR: A survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models.
Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed
1,575 citations
TL;DR: The proposed PSO method was indeed more efficient and robust in improving the step response of an AVR system and had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.
Abstract: In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.
1,485 citations
Book•
15 Feb 2002
TL;DR: Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms and Other Kinds of Evolutionary Fuzzies Applications.
Abstract: Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.
822 citations
TL;DR: In this paper, a 2 DOF planar robot was controlled by Fuzzy Logic Controller tuned with a particle swarm optimization and simulation results show that Fuzzies Logic Controller is better and more robust than the PID tuned by particle swarm optimized for robot trajectory control.
Abstract: In this paper, a 2 DOF planar robot was controlled by Fuzzy Logic Controller tuned with a particle swarm optimization. For a given trajectory, the parameters of Mamdani-type-Fuzzy Logic Controller (the centers and the widths of the Gaussian membership functions in inputs and output) were optimized by the particle swarm optimization with three different cost functions. In order to compare the optimized Fuzzy Logic Controller with different controller, the PID controller was also tuned with particle swarm optimization. In order to test the robustness of the tuned controllers, the model parameters and the given trajectory were changed and the white noise was added to the system. The simulation results show that Fuzzy Logic Controller tuned by particle swarm optimization is better and more robust than the PID tuned by particle swarm optimization for robot trajectory control.
231 citations