Toshinori Munakata

Fuzzy systems: an overview

Born in the United States around 1965, fuzzy set theory has grown to become a major scientific domain collectively referred to in this article as «fuzzy systems,» which include fuzzy sets, logic, algorithms, and control. For the past few years, particularly in japan, approximately 1,000 commercial and industrial fuzzy systems have been successfully developed. The number of industrial and commercial applications worldwide appears likely to increase significantly in the neat future (see Table 1) [6, 18]. Interest in the U.S. and other countries has also been growing recently, as indicated by both the first IEEE conference on fuzzy systems held in March 1992 and the first IEEE Transactions on Fuzzy Systems, which premiered in February 1993 (see Table 2)

Principles and applications of chaotic systems

There lies a behavior between rigid regularity and randomness based on pure chance. Its called a chaotic system, or chaos for short [5]. Chaos is all around us. Our notions of physical motion or dynamic systems have encompassed the precise clock-like ticking of periodic systems and the vagaries of dice-throwing chance, but have often been overlooked as a way to account for the more commonly observed chaotic behavior between these two extremes. When we see irregularity we cling to randomness and disorder for explanations. Why should this be so? Why is it that when the ubiquitous irregularity of engineering, physical, biological, and other systems are studied, it is assumed to be random and the whole vast machinery of probability and statistics is applied? Rather recently, however, we have begun to realize that the tools of chaos theory can be applied toward the understanding, manipulation, and control of a variety of systems, with many of the practical applications coming after 1990. To understand why this is true, one must start with a working knowledge of how chaotic systems behave—profoundly, but sometimes subtly different, from the behavior of random systems.

Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3

Abstract The rule extraction capability of neural networks is an issue of interest to many researchers. Even though neural networks offer high accuracy in classification and prediction, there are criticisms on the complicated and non-linear transformation performed in the hidden layers. It is difficult to explain the relationships between inputs and outputs and derive simple rules governing the relationships between them. As alternatives, some researchers recommend the use of rough sets or ID3 for rule extraction. This paper reviews and compares the rule extraction capabilities of rough sets with neural networks and ID3. We apply the methods to analyze expert heuristic judgments. Strengths and weaknesses of the methods are compared, and implications for the use of the methods are suggested.

Chaos computing: implementation of fundamental logical gates by chaotic elements

Basic principles of implementing the most fundamental computing functions by chaotic elements are described. They provide a theoretical foundation of computer architecture based on a totally new principle other than silicon chips. The fundamental functions are: the logical AND, OR, NOT, XOR, and NAND operations (gates) and bit-by-bit arithmetic operations. Each of the logical operations is realized by employing a single chaotic element. Computer memory can be constructed by combining logical gates. With these fundamental ingredients in hand, it is conceivable to build a simple, fast, yet cost effective, general-purpose computing device. Chaos computing may also lead to dynamic architecture, where the hardware design itself evolves during the course of computation.. The basic ideas are explained by employing a one-dimensional model, specifically the logistic map.

Flow resistance for microfluidic logic operations

Control of relative flow resistance is used for the actuation of both one- and two-input microfluidic “logical gates”. By taking advantage of system nonlinearities and despite the linear response of laminar flows associated with these length scales, a number of operators including the NOT, AND, OR, XOR, NOR, and NAND are demonstrated. Because these gates can be actuated simultaneously they can be combined to form more complicated devices such as a half adder. This approach is therefore flexible and illustrates that any macro- or microscale technique that can alter flow resistance can be used as the basis of a fluid-based logical micro-operator.

Micro/nanofluidic computing

Fluid-based computing may smooth the transition to microscale systems.

Rough Control: A Perspective

Observing the current state of commercial and industrial AI, control and hybrid systems are said to have the highest potentials for massive practical applications of rough set theory. After a brief description of the control problem and fuzzy systems, the principles of rough control and a scenario of fine temperature control are discussed.

Notes on implementing sets in Prolog

B orn in France in the early 1970s, Prolog (PROgramming in LOGic) [7, 11] was brought to world attention when chosen as the official language for the wellpublicized, Japans Fifth Generation Computer Project, in 1981. Currently, Prolog is one of the two major artificial intelligence languages, the other being Lisp. It is a simple, yet powerful declarative symbolic language based on predicate logic. Its application domain includes expert systems, natural language processing, compiler writing, symbolic algebra, VLSI circuit analysis, relational databases, and more recently, image processing. Prolog is sometimes used in an early stage of system development for quick implementation. A successful Prolog system that is likely to be used extensively is sometimes rewritten in C for faster computation. The language has been implemented in the form of compilers or interpreters on various generalpurpose computers ranging from PCs to mainframes (a few dozen exist today). Some extensions include more powerful facilities (e.g., Prolog III CLP--Constraint Logic Programming [2, 8, 9], and development of concurrent versions of Prolog (e.g., [ 15 ] Shared Prolog [4], Parlog[14]). Prolog has also been

Genetic Algorithms and Evolutionary Computing

A genetic algorithm is a technique for optimization; that is, it can be used to find the minimum or maximum of some arbitrary function. While there are a lar ge number of mathematical techniques for accomplishing this, both in general and for specific circumstances, a genetic algorithm is unique. It is a stochastic method, and it will find a global minimum, neither property being singular . The approach is remarkable because it is based on the way that a population of living or ganisms grows and evolves, fitting into their ecological niche better with each generation.

Notes on implementing fuzzy sets in Prolog

Due to its unique characteristics, Prolog requires special techniques for implementing ordinary as well as fuzzy sets. This article presents a comparative overview of various strategies of representing and manipulating fuzzy sets in Prolog. There are two major approaches to implement fuzzy sets in Prolog. One is to incorporate fuzzy representations and operations on top of an existing Prolog. The second way is to develop a new extended Prolog language. This article discusses various methods based primarily on the first approach. The choice of a method depends on many factors, such as whether a database for fuzzy sets already exists, the type of applications, the type of fuzzy set operations performed, whether an implicit description of the elements is possible, the size of the database, and the required computation time. The following methods are discussed in this article: explicit description (finite), using lists; explicit description (finite), using a fact for every element; implicit description (finite, infinite); and other methods and extensions.