Development Of Fuzzy System Research Articles

COMPUTATIONAL INTELLIGENCE AND ENVIRONMENTAL PLANNING

Even though the first publication in the area of fuzzy set theory (FST)—one of the ingredients of computational intelligence (CI)—appeared in 1965, the development of this theory for almost 20 years remained in the academic realm. Almost all basic concepts, theories, and methods were, however, developed during this period. Fuzzy control opened the gate to real applications for FST. Particularly in Japan, the applications of the fuzzy control principle in consumer goods made FST known in the public and made it commercially interesting for industry. This led to two developments: because the development of fuzzy applicational systems had to be efficient, fuzzy CASE tools and expert system shells were developed, making FST into fuzzy technology. The success in Japan could draw the attention of the media and started—first in Germany—the “fuzzy booms,” which led to an unprecedented growth in publications, university teaching, and other industrial applications in many countries. Around 1993, FST, neural nets, and evolutionary computing joined forces and were soon considered to be one area called soft computing, or CI. Applications in engineering as well as in management became very numerous. One of the areas in which CI is increasingly applied is environmental engineering, planning or science. The character of CI—particularly of FST—is described and real and potential applications of CI in this area are discussed.

Development of fuzzy controlled ABS systems for motorcycles

In this paper, we present the design and implementation of a new mechanism for an antilock brake system (ABS) pressure regulator on motorcycles. We use static tests to ensure the pressure regulation function. The dynamic test is constructed in the experimental bench for the closed-loop motorcycle ABS control. Furthermore, two fuzzy controllers are designed and tested in the platform (respectively). The first one directly uses the slip ratio error and its change as two inputs to the controller. However, in most implementations, the slip ratio is not available. Thus, the other controller that uses wheel speed and its change rate as inputs is also used in the experiments. The performances of these two control systems are verified in the platform. Both controllers can achieve their goal of tracking the desired slip ratio.

Fuzzy types: a framework for handling uncertainty about types of objects

Like other kinds of information, types of objects in the real world are often found to be filled with uncertainty and/or partial truth. It may be due to either the vague nature of a type itself or to incomplete information in the process determining it even if the type is crisp, i.e., clearly defined. This paper proposes a framework to deal with uncertainty and/or partial truth in automated reasoning systems with taxonomic information, and in particular type hierarchies. A fuzzy type is formulated as a pair combining a basic type and a fuzzy truth-value, where a basic type can be crisp or vague (in the intuitive sense). A structure for a class of fuzzy truth-value lattices is proposed for this construction. The fuzzy subtype relation satisfying intuition is defined as a partial order between two fuzzy types. As an object may belong to more than one (fuzzy) type, conjunctive fuzzy types are introduced and their lattice properties are studied. Then, for reasoning with fuzzy types, a mismatching degree of one (conjunctive) fuzzy type to another is defined as the complement of the relative necessity degree of the former to the latter. It is proved that the defined fuzzy type mismatching degree has properties similar to those of fuzzy set mismatching degree, which allow a unified treatment of fuzzy types and fuzzy sets in reasoning. The framework provides a formal basis for development of order-sorted fuzzy logic systems.

Stability analysis and development of a class of fuzzy control systems

The paper presents a development method for fuzzy controllers, derived from a stability analysis method based on a linearized model of the controlled plant. The stability conditions are expressed by a set of indices that verify the character of the equilibrium point and its uniqueness, and also measure the relative degree of stability. The proposed development method is applicable to the control of a class of nonlinear plants of arbitrary order, with the right-hand term of the state equation restricted to being a continuous and partially differentiable function. For the sake of simplicity, the presentation is mainly dedicated to second-order systems. The application examples presented here deal with the control of two real-world processes, namely the development of fuzzy control systems intended for the position control of an electro-hydraulic servosystem, and for the speed control of an electrical driving system with a variable moment of inertia.

Tuning of a neuro-fuzzy controller by genetic algorithm

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.

A multilevel weighted fuzzy reasoning algorithm for expert systems

The applications of fuzzy production rules (FPR) are rather limited if the relative degree of importance of each proposition in the antecedent contributing to the consequent (i.e., the weight) is ignored or assumed to be equal. Unfortunately, this is the case for many existing FPR and most existing fuzzy expert system development shells or environments offer no such functionality for users to incorporate different weights in the antecedent of FPR. This paper proposes to assign a weight parameter to each proposition in the antecedent of a FPR and a new fuzzy production rule evaluation method (FPREM) which generalizes the traditional method by taking the weight factors into consideration is devised. Furthermore, a multilevel weighted fuzzy reasoning algorithm (MLWFRA) incorporating this new FPREM, which is based on the reachability and adjacent place characteristics of a fuzzy Petri net, is developed. The MLWFRA has the advantages that i) it offers multilevel reasoning capability; ii) it allows multiple conclusions to be drawn if they exist; iii) it offers a new fuzzy production rule evaluation method; and iv) it is capable of detecting cycle rules.

Stability Analysis and Design of a Class of Fuzzy Control Systems

Abstract The paper presents a design method of fuzzy controllers (FCs) derived from a stability analysis method (Aracil, et al., 1989). The stability conditions are expressed by a number of indices which verifies the character of the equilibrium point, its uniqueness and also measures the relative degree of stability. The method is applied to the development of a fuzzy control system meant for the position control of an electrohydraulic servosystem.

Model-free optimization of fuzzy rule-based systems using evolution strategies

In this paper the applicability of evolution strategies, a special kind of evolutionary algorithms, to the problem of parameter optimization in the development of fuzzy rule-based systems is demonstrated. For this aim we introduce a shell which supports the design of any kind of rule based systems employing fuzzy logic for the formalization of imprecise reasoning processes and which optimizes all numerical parameters. This method works model-free, we do not need to know implicit features of the optimizing system.

Developing a new transformer fault diagnosis system through evolutionary fuzzy logic

To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

A fuzzy approach to mapping problems

This paper explores the possibility of using fuzzy logic to solve the problem of mapping in some significant cases. The basic idea is to exploit the fuzzy logic prerogative of accurately managing values of which only a qualitative estimate is known in such a way as to generate fuzzy inference rules for the heuristic solution for the mapping problem. The paper first deals with a simple case of mapping so as to describe the method used to deduce the fuzzy inference rules, considering both topological and cardinal variation. Then the method is applied to a toroidally connected network of processors. The proposed method is then implemented by means of a tool for the development of fuzzy systems through which it is possible to run the rule-based mapping system.

Parallel fuzzy information processing system

During the past decade, several specific hardwares for the fast fuzzy inference have been developed. However, most of them are dedicated to a specific inference method and thus cannot support fuzzy set operations. In this paper, we present a hardware architecture called KAFA (KAIST fuzzy accelerator) which provides various fuzzy inference methods and fuzzy set operations. The architecture has SIMD structure, which consists of two parts: a system control unit (MC: main controller) and an arithmetic unit (FPE: fuzzy processing element). The prototype of the proposed architecture was developed by using FPGA chips. Each FPE has a maximum speed of 5M FLOPS (fuzzy logic operations per second). As the KAFA contains 128 FPEs, we achieve a peak peformance of 640M FLOPS. The speed of the KAFA holds promise for the development of new fuzzy application systems.

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.

Fuzzy control of a hovercraft platform

A great majority of current applications of fuzzy logic are in fuzzy expert control systems. The candidate systems for fuzzy expert control can be characterized as systems that possess complex or unmodeled dynamics, high dimensionality, many interacting variables, system perturbations, or a combination of any of these. The hovercraft is one such system. The complexities that exist in hovercraft control arise because of the dual requirements of maintaining lift and maneuverability, as well as stability. Using fuzzy logic, the difficulties in both the design and the application of the control process are well managed. This paper demonstrates this through the implementation of the design and application of a fuzzy controller for stabilizing the response of a simple hovercraft platform. The physical platform constructed as a testbed is described, along with the associated control hardware. This is followed by a discussion of the fuzzy rule-based system development which resulted in a fuzzy controller using a total of twelve rules for achieving platform stability.

Computational intelligence standards: motivation, current activities and progress

Computational Intelligence is an emerging technology of keen interest to the developers of computer standards and interfaces. Coherent communications among the diverse set of users of computational AI is necessary for the protection of all parties and can help further the serious development of artificial neural networks, fuzzy systems, evolutionary programming and virtual reality. Current activities of the IEEE Neural Networks Council Standards Committee encompass all these areas, emphasizing the development of glossaries and symbologies, performance measures and interface standards for these interrelated fields. Progress toward these goals is described in this paper.

Adaptive fuzzy control of a water bath process with neural networks

Recently, the emergence of artificial neural networks has made it feasible to integrate fuzzy logic controllers and neural models for the development of adaptive fuzzy control systems. This paper proposes an adaptive fuzzy-neural control scheme by integrating two neural models with a basic fuzzy logic controller. Using the backpropagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the temperature control of a water bath process. Simulations show how the neural networks improve the performance of a poorly tuned basic fuzzy controller. The performance of the adaptive fuzzy-neural controller is compared to the basic fuzzy logic controller and a conventional digital-PI controller under identical conditions of varying complexities in the process. The experimental results show that the adaptive fuzzy-neural control scheme is superior in performance to the other two controllers.

Development of fuzzy excitation control system

AbstractPower system stability is essential to reliable and economical operation of power systems. The quick‐response excitation control with PSS is used as a power system stability improvement measure at the generator. However, this method is not always sufficient to apply in a wide operation range of power system configuration, load flow, and so on.This paper includes results of experiments of fuzzy excitation control system on the ac/dc Power System Simulator of CRIEPI. This control system is verified to improve power system stability and has better performance than the conventional excitation control on a one‐and two‐machine to infinite bus system.

Fuzzy fundamentals

An orderly design procedure that can save time and help prevent problems in the development of fuzzy logic systems is presented. The nature of fuzzy logic is examined, and the design of fuzzy control systems is discussed. The architecture of a simple fuzzy controller for a steam turbine is used as an example, to show how fuzzy control models work. A four-step methodology for fuzzy system design is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Derivation and application of an expert fuzzy optimal control system

This paper presents derivation of a fuzzy optimal controller which may be used to develop a knowledge based expert control system for supervising and tuning of conventional feedback control loops. A general theory, based on the conceptual framework for optimization in a fuzzy environment, is presented for the development of linear and nonlinear fuzzy optimal control systems. This technique is based on integration of the optimal control theorem and fuzzy variables. The resulting expert controller is expressed in the form of linguistic fuzzy rules, representing the knowledge base for tuning of the feedback control loops. Tuning inferences are made from the response of local feedback control loops by applying the compositional rule of inference to the knowledge base.

Development of fuzzy blood pressure control system

Fuzzy blood pressure control system has been developed to regulated blood pressure by using vaso-active drugs, which aims the medical treatment of the patient. Fuzzy logic is used to convert heuristic control rules as a physician's knowledge into an automatic control strategy. Usually, a physician decides infusion rate based on referring ideal pressure trajectory during control. To emulate such a physician's heuristic control, the controller refer an ideal pressure trajectory, for example, from the normal level to the target level. One of Inputs to the controller is a difference between real pressure and ideal pressure, where another is a rate of its pressure difference. An output from the controller is the change in drug infusion rate. Both of inputs and output are transformed into fuzzy sets, whose membership functions are predefined. Twenty one physician's control rules are converted into fuzzy control rules. The fuzzy control procedure consists of three steps: (1) computing both input values and finding their appropriate membership values (grade), (2) finding applied rules and applying fuzzy composition to infer weighted output fuzzy sets, (3) inferring the change in drug infusion rate by taking the center of mass of the membership function of output. Nine animal experiments are performed to evaluate the developed system by using rats with vasopressor drug. Angiotensin II Blood pressure was elevated from normal pressure (around 100 mmHg) to 150 mmHg. Satisfactory blood pressure control was realized on eight experiments and effectiveness of the controller was confirmed confirmed.(ABSTRACT TRUNCATED AT 250 WORDS)