Journal of Intelligent & Fuzzy Systems - Volume 7, issue 1

Authors: Klein, William B. | Westervelt, Robert T. | Luger, George F.

Article Type: Research Article

Abstract: Tuning and controlling particle accelerators is time consuming and expensive. Inherently nonlinear, this control problem is one to which conventional methods have not satisfactorily been applied; the result is constant and expensive monitoring by human operators. In recent years, and with isolated successes, advanced information technologies such as expert systems and neural networks have been applied to the individual pieces of this problem. Most advanced information technology attempts are also very special purpose and built in a manner not at all generalizable to other accelerator installations. In this paper, we discuss preliminary results of our research combining various methodologies from …the field of artificial intelligence into a general control system for accelerator tuning. We consider state space search and adaptive/learning algorithms including fuzzy logic, rule-based reasoning, neural networks, and genetic algorithms. We then propose a framework for applying these methods to a general purpose system for control. Finally, we discuss future plans for extending the system to include parallel distributed reasoning, an enhanced object structure, and additional heuristic control methods. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 1, pp. 1-12, 1999

Authors: Vishnupad, Prashant S. | Shin, Yung C.

Article Type: Research Article

Abstract: This paper presents an adaptive fuzzy tuner for the optimization of non-linear, multi-variable problems. The gradient-descent method is used to adaptively tune the bases of the membership functions used in the fuzzy logic optimization. The performance of the optimization with adaptive tuning is tested in comparison with fuzzy optimization without adaptive tuning of membership functions. It is shown that the adaptive fuzzy optimization provides better performance by converging to the optimal value in lesser time and fewer iterations. A multi-variable, non-linear optimization problem is shown as an illustrative example to demonstrate improved performance. This adaptive tuning scheme has also been …incorporated into the Generalized Intelligent Grinding Advisory System (GIGAS II) and results show that even for very complex problems such as manufacturing processes, improved performance can be obtained. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 1, pp. 13-25, 1999

Authors: Yager, Ronald R.

Article Type: Research Article

Abstract: Knowledge representation plays a fundamental role in the construction of artificial intelligence for modeling commonsense reasoning. We emphasize the role fuzzy logic can play in extending some of the models of AI. Some basic ideas from the fuzzy set based theory of approximate reasoning are introduced. We then discuss two extensions of the theory of approximate reasoning, possibilistic logic and effectation logic.

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 1, pp. 27-45, 1999

Authors: Jarrah, M.A. | Shaout, A.

Article Type: Research Article

Abstract: A fuzzy logic based automatic braking system is proposed using distance and relative speed sensors as inputs and brake-pressure as output. Heuristic rules have been developed and implemented. The controller monitors the deceleration rate of the vehicle to prevent tire lock-up and the consequent loss of directional stability. The system offers the flexibility of setting the separation distance. Simulation of the controller for driving into a stationary or moving objects shows that the system is performing well. It also uses an anti lock braking system to decelerate the vehicle and a throttle on-off controller to accelerate the vehicle and maintain …a fixed separation distance and drive behind the object in a tracking mode, i.e., adjusts the speed as obstacles occur. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 1, pp. 47-54, 1999

Authors: Pal, Nikhil R. | Pal (nee Dutta), Kuhu

Article Type: Research Article

Abstract: It may not always be possible for an expert to provide a set of completely consistent rules. Even if the rules are consistent, all rules may not have equal importance to control the system. Moreover, for a fuzzy controller, the rule-base is usually tuned through modification of membership functions. Effect of changing a membership function is global in the sense that it influences all rules that involve the membership function. Here we propose a very effective extension of the conventional fuzzy reasoning system with incorporation of an importance factor for each rule. This factor allows tuning of the system …at the rule level. Of course, one can still tune the membership functions. It enables the system to cope with incorrect and/or incompatible rules and thereby enhances the robustness, flexibility and system modeling capability. The proposed model is quite general and can be used in different applications including control. In the present investigation, we demonstrate with extensive simulation how for a control application inconsistent rules can be dealt with. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 1, pp. 55-73, 1999

Authors: Jenkins, David F. | Passino, Kevin M.

Article Type: Research Article

Abstract: While the design methodology for fuzzy controllers has proven itself in certain commercial and industrial applications, there is a significant need to perform mathematical analysis of fuzzy control systems prior to implementation: (i) to verify and certify their behavior so that, for example, instabilities can be avoided for applications demanding highly reliable operation such as aircraft and nuclear reactor control, and (ii) to provide insight to the expert on how to modify the fuzzy controller to guarantee that performance specifications are met (e.g., to guarantee a specified rise-time or the absence of steady state tracking error). In this paper we …provide a survey of, and an introduction to the area of nonlinear analysis of fuzzy control systems. We begin by overviewing several approaches to stability analysis including Lyapunov's Direct and Indirect Methods, and the Circle Criterion. We provide examples to illustrate how to design stable fuzzy control systems and test for stability, including an application of Lyapunov's direct method to Takagi–Sugeno fuzzy systems. Next, we introduce the idea of analyzing the steady state tracking error of a class of fuzzy control systems and provide examples of how to predict and reduce steady state error. Finally, we provide an introduction to the use of the describing function technique for the prediction of the existence, frequency, amplitude, and stability of limit cycles. We provide examples of limit cycle analysis and show how to design fuzzy controllers to avoid limit cycles. While our primary objective is to provide a control-theoretic introduction to, and survey of approaches to nonlinear analysis of fuzzy control systems where we utilize several existing results and provide useful tutorial examples, in the process we actually make contributions by providing, for example, the first results that show how to analyze steady state tracking error for fuzzy control systems. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 7, no. 1, pp. 75-103, 1999