Journal of Intelligent & Fuzzy Systems - Volume 10, issue 1

Authors: Stanley, Kevin | Wu, Q.M. Jonathan | Silva, Clarence W. de | Gruver, William A.

Article Type: Research Article

Abstract: One of the essential problems of feature-based visual servoing is calculating the inverse Jacobian, which relates changes in features to changes in robot position. Neural networks can approximate the inverse feature Jacobian. Neural networks also allow other forms of vision input to be easily used to position the robot. The vision system is primarily responsible for reducing the dimensionality of the input to reduce the size and therefore computational load on the system. In this paper …we develop a system which uses neural networks to both encode the image and generate control signals. In our system, the image dimensionality can be reduced in four ways: feature extraction, averaging compression, vector quantization, and principal component expansion. We demonstrate that it is possible to use neural networks for both image analysis and control of a vision guided robot, with little loss of accuracy when compared to feature based extraction. Show more

Citation: Journal of Intelligent & Fuzzy Systems, vol. 10, no. 1, pp. 1-11, 2001

Authors: Li, Jun | Parsons, Michael G.

Article Type: Research Article

Abstract: This paper investigates the applicability and feasibility of real-coded GAs for the complete development of fuzzy systems with imbedded constraints; i.e., automatic design of both fuzzy membership functions and rules subject to certain constraints. In this paper, a real-coded GA called RGA is developed, in which chromosomes are represented as real vectors. The constraints associated with the fuzzy systems are explicitly considered and satisfied during the training process of the fuzzy systems by the RGA. The …satisfaction of the constraints is done primarily by using tailor-made genetic operators, which are designed based on the coding structure and the required characteristics of the fuzzy systems. We apply the RGA to the design of three fuzzy systems, called fuzzy decision models (FDMs), to model and forecast economic activities in the crude oil tanker sector of maritime transportation. The effects of the RGA's control parameters on the performance of the RGA, including the crossover and mutation rates, multiple mutation, and selection pressure, are investigated in this paper. Our results indicate that the RGA is robust in developing appropriate FDMs with strong modeling and forecasting capability. Show more

Keywords: genetic algorithm, real-coded chromosome, tournament selection, crossover, mutation, fuzzy membership functions and fuzzy rules

Citation: Journal of Intelligent & Fuzzy Systems, vol. 10, no. 1, pp. 13-37, 2001

Authors: Tzafestas, S.G. | Rigatos, G.G. | Vagelatos, G.A.

Article Type: Research Article

Abstract: In this paper a novel fuzzy gain-scheduler is applied to linear plants the transfer functions of which consist of interval polynomials. The gain-scheduler performs an incremental tuning of the controller parameters employing a sliding-mode fuzzy control algorithm. The allowed ranges of variation of the controller gains are determined by using Kharitonov's Extremal Gain Margin Theorem. Sufficient conditions for the asymptotic stability of the closed-loop system are given. The validity of the method is verified via …simulation tests. Show more

Keywords: fuzzy gain scheduling, sliding-mode fuzzy logic control, Kharitonov's extremal gain theorem, interval polynomials, Lyapunov stability

Citation: Journal of Intelligent & Fuzzy Systems, vol. 10, no. 1, pp. 39-56, 2001

Authors: Myung, H.C. | Bien, Z. Zenn | Hirota, Kaoru

Article Type: Research Article

Abstract: HRIV (Hybrid Rule Interval Variation) method is presented, as a means of stabilizing a class of fuzzy control-based nonlinear systems. The HRIV method consists of two modes: I-mode (Minimum Approximation Error-based control) and R-mode (Rule Interval Variation-based control). In I-mode, sliding mode control is used to compensate for the minimum approximation error caused by heuristic characteristics of fuzzy logic control. In R-mode, RIV (Rule Interval Variation) method reduces the interval lengths of rules as the states …converge to an equilibrium point. The HRIV method considers the analytic upper bound of the minimum approximation error and reduces its effect in the control input, which is considered to be advantageous compared with the previous results. To show effectiveness of the proposed method, a simulation experiment is conducted to stabilize a simple nonlinear system and a modified inverted pendulum system. Show more

Citation: Journal of Intelligent & Fuzzy Systems, vol. 10, no. 1, pp. 57-73, 2001