Industrial Applications of Fuzzy Technology

by Kaoru Hirota, H. Solomon

Overview

The application of fuzzy technology is widely known as a technological revolution. Shortly after it appeared, its value has rapidly become appreciated. It is absolutely indispensable for introducing the latest developments not only domestically but also internationally. This book is arranged to introduce easy to understand explanations mainly centered on concrete applications. It consists of twelve chapters in total which are all independently readable and provide different approaches on various projects. The minimum of Fuzzy Theory that is needed to understand its practical applications is given in Chapter 1. Chapters 2 to 5 discuss hardware, including chips, and software tools used in constructing system. Chapters 6 to 12 cover a series of practical applications. These in clude applications for industrial processes and plants, transportation systems, which were among the first applications, and applications for consumer products such as household electrical appliances. These elements together finally produced the worldwide "Fuzzy Boom". This book can be read by a wide variety of people, from undergraduate and graduate students in universities to practical engineers and project managers working in plants. The information contained in this book is a first step to this field of interest.

Product Details

ISBN-13:	9784431658795
Publisher:	Springer Japan
Publication date:	06/22/2012
Edition description:	Softcover reprint of the original 1st ed. 1993
Pages:	312
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1 The Basis of Fuzzy Theory.- 1.1 Introduction.- 1.2 Fuzzy set theory.- 1.2.1 Review of crisp sets.- 1.2.2 Fuzzy sets.- 1.3 Fuzzy inference.- 1.4 Conclusion.- 2 ERIC A Shell for Real-time Process Control.- 2.1 Background.- 2.2 The design of ERIC.- 2.2.1 Information processing for control operations.- 2.2.2 Installation in a real-time computer system.- 2.3 Internal composition of the shell.- 2.4 ERIC’s knowledge expressions.- 2.4.1 Working memory.- 2.4.2 Rule sets.- 2.5 An overview of ERIC inference processing.- 2.5.1 Rule setting processing.- 2.6 Fuzzy processing in ERIC.- 2.6.1 Fuzzy logic, which is suited for process control.- 2.6.2 Fuzzy inference processing in ERIC.- 2.7 Functions for real-time control.- 2.8 In conclusion.- 3 Model Base Fuzzy Inference.- 3.1 General concepts.- 3.1.1 Introduction.- 3.1.2 Multi-purpose systems and intelligence.- 3.1.3 Human problem-solving processing.- 3.1.4 IS intellectual levels.- 3.2 Model — based fuzzy inference.- 3.2.1 Outline of model-based fuzzy inference.- 3.2.2 Instructions and reporting among intellectual levels.- 3.2.3 Differences between IS intellectual levels.- 3.3 Model-based fuzzy inference in intellectual level 2.- 3.3.1 Roles and observations (I2).- 3.3.2 Intellectual level 2 dynamics.- 3.3.3 State-space (I2, D2).- 3.3.4 Action plans (D2, S2).- 3.4 Adaptive systems.- 3.4.1 Learning.- 3.4.2 Fuzzy adaptation.- 3.5 Intellectual level 3, 4 model — based fuzzy inference.- 3.5.1 Goal — oriented inference systems [7].- 3.5.2 Intellectual level 3 condition judgment.- 3.5.3 Intellectual level 4 decision making.- 3.5.4 Intellectual level 3 goal determination.- 3.6 In conclusion.- 4 Fuzzy Development Stations and Fuzzy Inference Processors.- 4.1 Background to development.- 4.2 Characteristics of the Mycom Fuzzy Work Station.- 4.2.1 Simulation.- 4.2.2 Flexibility.- 4.2.3 Easy to operate.- 4.2.4 The fuzzy inference engine.- 4.3 Configuration of the Mycom Fuzzy Station.- 4.3.1 Host computer (Table 4.1).- 4.3.2 Development support software (Table 4.2).- 4.3.3 Communication expansion board.- 4.3.4 Fuzzy controller (FBEN, FCAS).- 4.3.5 Fuzzy inference processors for special uses.- 4.3.6 Signal processing board.- 4.4 Fuzzy Work Station functions.- 4.4.1 Define Membership Function.- 4.4.2 Edit Production Rules.- 4.4.3 Calculate Fuzzy Operations.- 4.4.4 Start Simulation.- 4.4.5 Compile to Transmitter-file.- 4.4.6 Implementation to Emulator (Communicate with FCAS) (standard specifications).- 4.4.7 Execution (engine specifications) (Start Fuzzy Control System with Input/Output Board).- 4.4.8 Verification of differences among fuzzy operation methods with the Fuzzy Work Station.- 4.4.9 Miscellaneous.- 4.5 Characteristics of the Virtual Paging Fuzzy Inference Chip.- 4.5.1 Characteristics of the Special Use Fuzzy Inference Chip.- 4.5.2 Future fuzzy inference processors.- 4.6 Summary.- 5 Fuzzy Processors.- 5.1 Introduction.- 5.2 The FP-3000 digital fuzzy processor.- 5.2.1 Outline of the FP-3000.- 5.2.2 Application examples.- 5.2.3 Development support tools.- 5.3 Analogue fuzzy processors.- 5.3.1 Outline of the analogue fuzzy hybrid IC.- 5.3.2 The TG005MC Inference chip.- 5.3.3 The TB01OPL Defuzzification chip.- 5.3.4 Development support tools.- 5.4 In conclusion.- 6 Fuzzy Controllers and Their Application to Water Treatment.- 6.1 Introduction.- 6.2 The general fuzzy controller design procedure.- 6.2.1 What fuzzy control is.- 6.2.2 Fuzzy inference methods.- 6.2.3 Design of control rules.- 6.2.4 Fuzzy controllers.- 6.3 The FRUITAX general purpose fuzzy control system.- 6.3.1 Development of FRUITAX.- 6.3.2 The FRUITAX series.- 6.3.3 Functions.- 6.3.4 Application fields.- 6.3.5 The development of FRUITAX-L.- 6.4 An example of fuzzy control in the water treatment field.- 6.5 Cooperative control of rain water pumps by an adaptive type controller.- 6.5.1 Outline of a rain water pumping station.- 6.5.2 Fuzzy control of pumping stations.- 6.5.3 Pumping station coordination simulation.- 6.6 In conclusion.- 7 A Combustion Control System for a Refuse Incineration Plant.- 7.1 Introduction Fuzziness incorporated into a refuse incineration plant.- 7.2 Characteristics of refuse incineration.- 7.3 Fuzzy control methods and problems.- 7.3.1 Fuzzy inference methods.- 7.3.2 Characteristics and problems of fuzzy inference.- 7.3.3 An ordinal structure model of control rules.- 7.4 A fuzzy control system.- 7.4.1 Composition of the fuzzy control system.- 7.4.2 Fuzzy sensors.- 7.4.3 Fuzzy control rules.- 7.5 An actual incinerator test.- 7.6 In conclusion.- 8 Fuzzy Control For Japanese Sake Fuzzy decision controller and fuzzy simulator for Japanese sake fermentation.- 8.1 Introduction.- 8.1.1 Background.- 8.1.2 On the Sake brewing process.- 8.2 Developing a fuzzy dicision system to perform Japanese sake fermentation control.- 8.2.1 Analysis samples.- 8.2.2 Result of brewing unrefined sake in the model brewery.- 8.2.3 Conversion to fuzzy control rules.- 8.2.4 Fuzzy simulator construction ..- 8.3 Test brewing using a pilot plant.- 8.3.1 Brewing conditions.- 8.3.2 Test brewing by manual operation.- 8.3.3 Test brewing using fuzzy control.- 8.4 Commercial scale application.- 8.4.1 Brewing conditions.- 8.4.2 Test results.- 8.5 Summary.- 9 Elevator Control Using a Fuzzy Rule Base.- 9.1 Introduction.- 9.2 Outline of elevator group control.- 9.2.1 What is a group control system?.- 9.2.2 Procedure for determining which cage to assign.- 9.3 An elevator group control system using a fuzzy rule base.- 9.3.1 System construction concept.- 9.3.2 Fuzzy rule base construction and action.- 9.3.3 Computation of rule degrees of applicability, and execution examples.- 9.3.4 Rule extraction.- 9.4 A simulation example.- 9.5 In conclusion.- 10 A Highway Tunnel Ventilation Control System Using Fuzzy Control.- 10.1 Introduction.- 10.2 Outline of a longitudinal flow ventilation system.- 10.3 A ventilation control system using fuzzy control.- 10.3.1 Traffic volume prediction.- 10.3.2 The ventilation operation plan.- 10.3.3 Judgment regarding change of ventilating machine combination.- 10.3.4 Fuzzy air flow speed and concentration control.- 10.3.5 Level control.- 10.3.6 Emergency control.- 10.4 Results of applying this system.- 10.5 Future problems.- 11 Fuzzy Control and Examples of Applications.- 11.1 Introduction.- 11.2 Trends in markets and technology.- 11.2.1 Trends in markets.- 11.2.2 Technological trends.- 11.3 Skilled operator’s operation and fuzzy control system.- 11.3.1 Skilled operator’s operation.- 11.3.2 Fuzzy control systems.- 11.4 Examples of applications of predictive fuzzy control systems.- 11.4.1 Application to an automatic train operation system.- 11.4.2 Automatic container crane operation system.- 11.4.3 A highway tunnel ventilation control system.- 11.4.4 A fuzzy fully automatic washing machine.- 11.5 Future expectations.- 11.6 In conclusion.- 12 Application of Fuzzy Theory to Home Appliances.- 12.1 Introduction.- 12.2 Fuzzy inference simplification methods and tuning methods.- 12.2.1 Simplification of fuzzy inference.- 12.2.2 Tuning by means of a neural network.- 12.3 Application to electrical appliances.- 12.3.1 A fuzzy fully automatic washing machine.- 12.3.2 A fuzzy vacuum cleaner.- 12.3.3 Future development of fuzzy household electrical appliances.- 12.4 Application to video equipment.- 12.5 In conclusion.