Some aspects of synthesis of probabilistic fuzzy controllers

This paper deals with some aspects of analysis and design problems of a class of control algorithms called probabilistic fuzzy controllers.The notion of random intersection coefficient is proposed here.The decomposition of a multi-dimensional probabilistic fuzzy controller is discussed as well. Such decomposition provides uniform expressions for both single-input, single-output and multi-dimensional probabilistic fuzzy controllers and it improves the computational efficiency.As an example, a two-input and two-output control system is presented as well.     

Design of fuzzy logic controllers based on generalized T-operators

Since Zadeh first proposed the basic principle of fuzzy logic controllers in 1968, the and operators have been popular in the design of fuzzy logic controllers. In this paper, the general concept of T-operators is introduced into the conventional design methods for fuzzy logic controllers so that a general and flexible methodology for the design of these fuzzy logic controllers is available. Then, by computer simulations, studies are made so as to determine the relations between the various T-operators and the performance of a fuzzy logic controller. It is concluded that the performance of the fuzzy logic controller for a given class of plants very much depends upon the choice of the T-operators.     

直流电机电流控制中两种模糊控制器的比较

提供了两种直流电机模糊控制器,其中一个采用了极大反模糊器而另一个采用了中心平均模糊器,我们研究了这两种模糊控制器对直流电机进行控制的过程和结果,最后得出了第二种控制器优于第一种控制器。     

Robust design of rule-based fuzzy controllers

This paper deals with the design of stable and robust rule-based fuzzy control systems. New expressions to compute indices which provide a measure of the stability and robustness of fuzzy control systems are presented. The relation between the modification of the rules and the stability is studied through the so-called sensitivity indices. The paper presents procedures that make use of these indices to improve the design of fuzzy control systems, including the modification of the rules to obtain the global stability of an unstable system with multiple attractors, and to improve the dynamic behavior or the robustness of a non-linear plant. An example with a fuzzy controller for a system with non-linear damping and saturation in the actuation is presented to illustrate the design procedure.     

一种新的三角模糊数算子在加权模糊推理中的应用

针对基于模糊逻辑的加权模糊推理,Chen Shy i-M ing提出了两种计算合取式前件整体真值的方法。由于所用模糊数算子的影响,两种方法的求取结果在准确性和合理性上都存在一定的缺陷。这种缺陷将直接影响推理的性能。因此,为改善这种缺陷,提高推理性能,本文提出了一种新的三角模糊数算子。它的应用可以提高推理的准确性和合理性。     

A hardware-friendly arithmetic method and efficient implementations for designing digital fuzzy adders

A new hardware-friendly mathematical method for realizing low-complexity universal Adder cells as well as its efficient hardware implementations is proposed in this paper. This method can be used in binary logic, Multiple-Valued Logic (MVL) and specifically digital fuzzy systems. The proposed mathematical method can be implemented in both voltage and current modes. The voltage-mode hardware implementation is very simple and is based on input capacitors and MVL or analog inverters and buffers. In addition, the current-mode hardware implementation leads to simple and efficient structures for digital fuzzy systems. Simulations are carried out for ternary logic as well as for digital fuzzy logic with high precision by using 180 nm standard CMOS technology and at 1.8 V supply voltage. Simulation results demonstrate that the proposed designs have excellent functionality and are very suitable for implementing MVL and fuzzy arithmetic circuits.     

G—R型模糊命题逻辑系统完备的一个充分必要条件

引入基模糊公式的概念,在Gaines-Rescher蕴函算子下,给出模糊命题逻辑系统完备的一个充分必要条件。     

Fuzzy relation equations theory as a basis of fuzzy modelling: An overview

We give a wide overview on the applications of fuzzy relation equations theory to decision-making processes, to the construction of preference relations and to Knowledge Engineering, mainly fuzzy control and fuzzy pattern recognition. General theoretical results and methodological aspects are pointed out.     

Fuzzy polynomial regression with fuzzy neural networks

Recently, fuzzy linear regression is considered by Mosleh et al. [1]. In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy polynomial regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

A self-organizing adaptive fuzzy controller

There are two main parts in this paper. The first part presents a knowledge representation and reasoning scheme, called tree-searched neural networks (TNN). The TNN is based on a well-known intuitive knowledge representation (IKR) and can reduce the number of the processing nodes in the neural networks. The second part proposes a self-organizing adaptive fuzzy controller (SOAFC) based on the TNN model. It can help acquire control knowledge and thus can reduce the dependence on experts. Furthermore, designers do not need to predefine all membership functions to cover whole input space domain. For improving its performance further, we design a D-controller which is included within the SOAFC. Whether the fuzzy controller is incorporated with the D-controller or not, it is also guaranteed to be globally stable. Simulation results show that this approach has faster convergence speed, results in better transient response, and in addition requires less total control energy.     

Selection of parameters for a fuzzy logic controller

The fuzzy logic controller is reviewed and its parameter are explicitly identified. The problem of initial selection and subsequent adjustment of the parameters are discussed in detail by example.     

A novel analog fuzzy controller for intelligent sensors

A new analog fuzzy logic controller implemented in CMOS technology is described. The chosen membership function generator keeps the needed area for the inference engine very small while giving a big flexibility in the configuration of the membership function. The proposed solution for defuzzification gives an additional area reduction over earlier implementations. High speed, low power fuzzy controller hardware make the chip appropriate for intelligent sensor application. Simulation results as well as test measurements are presented and discussed to illustrate the properties and robustness of the proposed circuit.     

Fuzzy sets and the modelling of physician decision processes,part II: fuzzy diagnosis decision models

Our concern is with the development of a more accurate mathematical model of physician diagnosis decision processes. Using the complete information net of matrices outputed from Part I, the various diagnoses processes—medical hypothesis, initial preliminary diagnoses, other preliminary diagnosis and final diagnoses, are modelled as fuzzy matrices. A general fuzzy decision model which could be particularized to each of the foregoing cases is then developed. Our general technique is based on fuzzy clustering theory with the criterion function being a modified form of the minimum Minkowski metric.     

Logistics network design for product recovery in fuzzy environment

The design of product recovery network is one of the important and challenging problems in the field of reverse logistics. Some models have been formatted by researchers under deterministic environment. However, uncertainty is inherent during the process of the practical product recovery. In order to deal with uncertainty, this paper employs a fuzzy programming tool to design the product recovery network. Based on different criteria, three types of optimization models are proposed and some properties of them are investigated. To solve the proposed models, we design a hybrid intelligent algorithm which integrates fuzzy simulation and genetic algorithm. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed models and algorithm.     

再扩充模糊逻辑中逻辑程序的模型论语义和不动点语义研究

模糊逻辑及其扩充是处理不确定性与模糊性信息的重要数学工具,在近似推理、人工智能等领域有着广泛的应用。而逻辑程序也已经成为人工智能的研究热点之一。本文是在再扩充模糊逻辑中,对逻辑程序进行了语义的研究。给出了其语法和语义描述,并且将逻辑程序的许多主要结论推广到再扩充模糊逻辑中。首先,得到了关于模糊逻辑推论的充分必要条件以及模型交与强模型交性质。然后,通过引入一个H erbrand解释算子Pτ:LBp→LBp,给出了确定性程序P的H erbrand模型的充分条件和强H erbrand模型的充分必要条件。最后,建立了确定性程序的最小强H erbrand模型的不动点刻画定理。     

On fuzzy multi-valued functions, part 4: Fuzzy economic models

An application to fuzzy economic systems of the fuzzy multi-valued functions theory elaborated by both of us in our previous papers is presented. First, stationary and dynamic fuzzy economic models are defined. Next the optimality of fuzzy trajectories of these models is introduced and investigated.