Fuzzy expert systems and fuzzy reasoning

Preface. 1 Introduction. 1.1 Characteristics of Expert Systems. 1.2 Neural Nets. 1.3 Symbolic Reasoning. 1.4 Developing a Rule-Based Expert System. 1.5 Fuzzy Rule-Based Systems. 1.6 Problems in Learning How to Construct Fuzzy Expert Systems. 1.7 Tools for Learning How to Construct Fuzzy Expert Systems. 1.8 Auxiliary Reading. 1.9 Summary. 1.10 Questions. 2 Rule-Based Systems: Overview. 2.1 Expert Knowledge: Rules and Data. 2.2 Rule Antecedent and Consequent. 2.3 Data-Driven Systems. 2.4 Run and Command Modes. 2.5 Forward and Backward Chaining. 2.6 Program Modularization and Blackboard Systems. 2.7 Handling Uncertainties in an Expert System. 2.8 Summary. 2.9 Questions. 3 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: I. 3.1 Classical Logic. 3.2 Elementary Fuzzy Logic and Fuzzy Propositions. 3.3 Fuzzy Sets. 3.4 Fuzzy Relations. 3.5 Truth Value of Fuzzy Propositions. 3.6 Fuzzification and Defuzzification. 3.7 Questions. 4 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: II. 4.1 Introduction. 4.2 Algebra of Fuzzy Sets. 4.3 Approximate Reasoning. 4.4 Hedges. 4.5 Fuzzy Arithmetic. 4.6 Comparisons between Fuzzy Numbers. 4.7 Fuzzy Propositions. 4.8 Questions. 5 Combining Uncertainties. 5.1 Generalizing AND and OR Operators. 5.2 Combining Single Truth Values. 5.3 Combining Fuzzy Numbers and Membership Functions. 5.4 Bayesian Methods. 5.5 The Dempster-Shafer Method. 5.6 Summary. 5.7 Questions. 6 Inference in an Expert System I. 6.1 Overview. 6.2 Types of Fuzzy Inference. 6.3 Nature of Inference in a Fuzzy Expert System. 6.4 Modification and Assignment of Truth Values. 6.5 Approximate Reasoning. 6.6 Tests of Procedures to Obtain the Truth Value of a Consequent from the Truth Value of Its Antecedent. 6.7 Summary. 6.8 Questions. 7 Inference in a Fuzzy Expert System II: Modification of Data and Truth Values. 7.1 Modification of Existing Data by Rule Consequent Instructions. 7.2 Modification of Numeric Discrete Fuzzy Sets: Linguistic Variables and Linguistic Terms. 7.3 Selection of Reasoning Type and Grade-of-Membership Initialization. 7.4 Fuzzification and Defuzzification. 7.5 Non-numeric Discrete Fuzzy Sets. 7.6 Discrete Fuzzy Sets: Fuzziness, Ambiguity, and Contradiction. 7.7 Invalidation of Data: Non-monotonic Reasoning. 7.8 Modification of Values of Data. 7.9 Modeling the Entire Rule Space. 7.10 Reducing the Number of Classification Rules Required in the Conventional Intersection Rule Configuration. 7.11 Summary. 7.12 Questions. 8 Resolving Contradictions: Possibility and Necessity. 8.1 Definition of Possibility and Necessity. 8.2 Possibility and Necessity Suitable for MultiStep Rule-Based Fuzzy Reasoning. 8.3 Modification of Truth Values During a Fuzzy Reasoning Process. 8.4 Formulation of Rules for Possibility and Necessity. 8.5 Resolving Contradictions Using Possibility in a Necessity-Based System. 8.6 Summary. 8.7 Questions. 9 Expert System Shells and the Integrated Development Environment (IDE). 9.1 Overview. 9.2 Help Files. 9.3 Program Editing. 9.4 Running the Program. 9.5 Features of General-Purpose Fuzzy Expert Systems. 9.6 Program Debugging. 9.7 Summary. 9.8 Questions. 10 Simple Example Programs. 10.1 Simple FLOPS Programs. 10.2 Numbers.fps. 10.3 Sum.fps. 10.4 Sum.par. 10.5 Comparison of Serial and Parallel FLOPS. 10.6 Membership Functions, Fuzzification and Defuzzification. 10.7 Summary. 10.8 Questions. 11 Running and Debugging Fuzzy Expert Systems I: Parallel Programs. 11.1 Overview. 11.2 Debugging Tools. 11.3 Debugging Short Simple Programs. 11.4 Isolating the Bug: System Modularization. 11.5 The Debug Run. 11.6 Interrupting the Program for Debug Checks. 11.7 Locating Program Defects with Debug Commands. 11.8 Summary. 11.9 Questions. 12 Running and Debugging Expert Systems II: Sequential Rule-Firing. 12.1 Data Acquisition: From a User Versus Automatically Acquired. 12.2 Ways of Solving a Tree-Search Problem. 12.3 Expert Knowledge in Rules auto1.fps. 12.4 Expert Knowledge in a Database: auto2.fps. 12.5 Other Applications of Sequential Rule Firing. 12.5.1 Missionaries and Cannibals. 12.6 Rules that Make Themselves Refireable: Runaway Programs and Recursion. 12.7 Summary. 12.8 Questions. 13 Solving "What?" Problems when the Answer is Expressed in Words. 13.1 General Methods. 13.2 Iris.par: What Species Is It? 13.3 Echocardiogram Pattern Recognition. 13.4 Schizo.par. 13.5 Discussion. 13.6 Questions. 14 Programs that Can Learn from Experience. 14.1 General Methods. 14.2 Pavlov1.par: Learning by Adding Rules. 14.3 Pavlov2.par: Learning by Adding Facts to Long-Term Memory. 14.4 Defining New Data Elements and New: RULEGEN.FPS. 14.5 Most General Way of Creating New Rules and Data Descriptors. 14.6 Discussion. 14.7 Questions. 15 Running On-Line in Real-Time. 15.1 Overview of On-Line Real-Time Work. 15.2 Input/Output On-Line in Real-Time. 15.3 On-Line Real-Time Processing. 15.4 Types of Rules Useful in Real-Time On-Line Work. 15.5 Memory Management. 15.6 Development of On-Line Real-Time Programs. 15.7 Speeding Up a Program. 15.8 Debugging Real-Time Online Programs. 15.9 Discussion. 15.10 Questions. Appendix. Answers. References. Index.

[1]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[2]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[3]  Rudolf Carnap,et al.  Philosophical Foundations of Physics an Introduction to the Philosophy of Science , 1966 .

[4]  L. Zadeh.  A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges , 1972 .

[5]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[6]  Richard Bellman,et al.  On the Analytic Formalism of the Theory of Fuzzy Sets , 1973, Inf. Sci..

[7]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[8]  E. H. Mamdani,et al.  Advances in the linguistic synthesis of fuzzy controllers , 1976 .

[9]  E. Shortliffe.  Computer-based medical consultations: mycin (elsevier north holland , 1976 .

[10]  Edward H. Shortliffe,et al.  Computer-based medical consultations, MYCIN , 1976 .

[11]  T. Pavlidis,et al.  Fuzzy sets and their applications to cognitive and decision processes , 1977 .

[12]  Elliott Mendelson,et al.  Introduction to Mathematical Logic , 1979 .

[13]  Ramon E. Moore.  Methods and applications of interval analysis , 1979, SIAM studies in applied mathematics.

[14]  John P. McDermott,et al.  RI: an Expert in the Computer Systems Domain , 1980, AAAI.

[15]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[16]  Brian R. Gaines,et al.  Fuzzy reasoning and its applications , 1981 .

[17]  Charles L. Forgy,et al.  Rete: A Fast Algorithm for the Many Patterns/Many Objects Match Problem , 1982, Artif. Intell..

[18]  Enrique H. Ruspini,et al.  Possibility theory approaches for advanced information systems , 1982, Computer.

[19]  Roger C. Schank.  The Cognitive Computer: On Language, Learning, and Artificial Intelligence , 1984 .

[20]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[21]  Mutsuo M. Yanase,et al.  Fuzziness and Probability , 1985 .

[22]  Elaine Kant,et al.  Programming expert systems in OPS5 , 1985 .

[23]  Peter Jackson,et al.  Introduction to expert systems , 1986 .

[24]  Klaus-Peter Adlassnig,et al.  Fuzzy Set Theory in Medical Diagnosis , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  James J. Buckley,et al.  A Parallel Rule Firing Fuzzy Production System with Resolution of Memory Conflicts by Weak Fuzzy Monotonicity, Applied to the Classification of Multiple Objects Characterized by Multiple Uncertain Features , 1987, Int. J. Man Mach. Stud..

[26]  Madan M. Gupta,et al.  Fuzzy Logic in Knowledge-Based Systems, Decision and Control , 1988 .

[27]  James J. Buckley,et al.  Echocardiogram analysis using fuzzy sets and relations , 1988 .

[28]  James J. Buckley,et al.  Managing Uncertainty in a Fuzzy Expert System , 1988, Int. J. Man Mach. Stud..

[29]  Didier Dubois,et al.  Possibility Theory - An Approach to Computerized Processing of Uncertainty , 1988 .

[30]  Tarun Khanna,et al.  Foundations of neural networks , 1990 .

[31]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .

[32]  Abraham Kandel,et al.  Fuzzy Expert Systems , 1991 .

[33]  A. Carlisle Scott,et al.  Practical guide to knowledge acquisition , 1991 .

[34]  오경환,et al.  [特輯]퍼지 추론(Fuzzy Reasoning) , 1992 .

[35]  Bruce G. Buchanan,et al.  DENDRAL and Meta-DENDRAL: Roots of Knowledge Systems and Expert System Applications , 1993, Artif. Intell..

[36]  Abraham Kandel,et al.  Fuzzy Control Systems , 1993 .

[37]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[38]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[39]  Michio Sugeno,et al.  Applied fuzzy systems , 1994 .

[40]  Charles Elkan,et al.  The paradoxical success of fuzzy logic , 1993, IEEE Expert.

[41]  Guido Deboeck,et al.  Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets , 1994 .

[42]  Earl Cox,et al.  The fuzzy systems handbook - a practitioner's guide to building, using, and maintaining fuzzy systems , 1994 .

[43]  Trevor P Martin,et al.  Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence , 1995 .

[44]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems , 1995 .

[45]  Earl D. Cox,et al.  Fuzzy Logic for Business and Industry , 1995 .

[46]  Clarence W. de Silva,et al.  Intelligent Control: Fuzzy Logic Applications , 1995 .

[47]  C. V. Altrock.  Fuzzy logic and neurofuzzy applications explained , 1995 .

[48]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[49]  Witold Pedrycz,et al.  Fuzzy sets engineering , 1995 .

[50]  Lotfi A. Zadeh,et al.  On the analysis of large-scale systems , 1996 .

[51]  George J. Klir,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems - Selected Papers by Lotfi A Zadeh , 1996, Advances in Fuzzy Systems - Applications and Theory.

[52]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[53]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[54]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[55]  Uzay Kaymak,et al.  Fuzzy logic control , 1997 .

[56]  Cornelius T. Leondes,et al.  Fuzzy logic and expert systems applications , 1997, Neural network systems techniques and applications.

[57]  W. Pedrycz,et al.  An introduction to fuzzy sets : analysis and design , 1998 .

[58]  James J. Buckley,et al.  A new t-norm , 1998, Fuzzy Sets Syst..

[59]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[60]  James E. Andrews,et al.  Combinatorial rule explosion eliminated by a fuzzy rule configuration , 1998, IEEE Trans. Fuzzy Syst..

[61]  R. Słowiński.  Fuzzy sets in decision analysis, operations research and statistics , 1999 .

[62]  James J. Buckley,et al.  Fuzzy and Neural: Interactions and Applications , 1999 .

[63]  Michael Negnevitsky.  Building fuzzy expert systems , 1999 .

[64]  James J. Buckley,et al.  L∞ fuzzy logic , 1999, Fuzzy Sets Syst..

[65]  Robert Babuska,et al.  Fuzzy Logic Control: Advances in Applications , 1999 .

[66]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[67]  Charles C. Ragin,et al.  Fuzzy-Set Social Science , 2001 .

[68]  Michael Wagenknecht,et al.  Fuzzy control : theory and practice , 2000 .

[69]  J. Aracil,et al.  Stability Issues in Fuzzy Control , 2000 .

[70]  W. Siler,et al.  Hemodynamic alarm system for pulmonary artery catheters in an intensive care unit , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[71]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[72]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[73]  H. Chertkow,et al.  Semantic memory , 2002, Current neurology and neuroscience reports.

[74]  Christer Carlsson,et al.  Fuzzy reasoning in decision making and optimization , 2001, Studies in Fuzziness and Soft Computing.

[75]  James J. Buckley,et al.  Fuzzy Probabilities , 2003 .

[76]  Robert J. Marks,et al.  Layered URC fuzzy systems: a novel link between fuzzy systems and neural networks , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[77]  Christopher R. Hitchcock.  Introduction: What is the Philosophy of Science , 2004 .

[78]  G. Klir,et al.  Fuzzy logic in geology , 2004 .

[79]  John Fulcher,et al.  Computational Intelligence: An Introduction , 2008, Computational Intelligence: A Compendium.