Industrial Applications of Fuzzy Technology

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.