Hybrid soft computing systems: where are we going?

Soft computing is an association of computing methodologies that includes fuzzy logic, neuro-computing, evolutionary computing, and probabilistic computing. After a brief overview of Soft Computing components, we will analyze some of its most synergistic combinations. We will emphasize the development of smart algorithm-controllers, such as the use of fuzzy logic to control the parameters of evolutionary computing and, conversely, the application of evolutionary algorithms to tune fuzzy controllers. We will focus on three real-world applications of soft computing that leverage the synergism created by hybrid systems.

[1]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[2]  M. Bitterman.  THE EVOLUTION OF INTELLIGENCE. , 1965, Scientific American.

[3]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[5]  Yann LeCun,et al.  Une procedure d'apprentissage pour reseau a seuil asymmetrique (A learning scheme for asymmetric threshold networks) , 1985 .

[6]  Didier Dubois,et al.  Soft computing, fuzzy logic, and artificial intelligence , 1998, Soft Comput..

[7]  KuipersBenjamin.  Commonsense reasoning about causality: deriving behavior from structure , 1984 .

[8]  Piero P. Bonissone,et al.  Financial applications of fuzzy case-based reasoning to residential property valuation , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[9]  Piero P. Bonissone,et al.  Industrial applications of fuzzy logic at General Electric , 1995, Proc. IEEE.

[10]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[11]  Hugues Bersini,et al.  Comparing RBF and Fuzzy Inference Systems on theoretical and practical basis , 1995 .

[12]  Witold Pedrycz,et al.  Handbook of fuzzy computation , 1998 .

[13]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[14]  Edward H. Shortliffe,et al.  A model of inexact reasoning in medicine , 1990 .

[15]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[16]  D. McDowell.  Foreword , 1999 .

[17]  I. S. Gradshtein,et al.  THE ELEMENTS OF MATHEMATICAL LOGIC , 1963 .

[18]  Russell Beale,et al.  Handbook of Neural Computation , 1996 .

[19]  Lotfi A. Zadeh,et al.  Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems , 1998, Soft Comput..

[20]  Piero P. Bonissone,et al.  Genetic algorithms for automated tuning of fuzzy controllers: a transportation application , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[21]  J. Reed,et al.  Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing. , 1967, Journal of theoretical biology.

[22]  John H. Holland,et al.  Outline for a Logical Theory of Adaptive Systems , 1962, JACM.

[23]  G. Cowles.  Studies of Mascarene Island birds: The fossil record , 1987 .

[24]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

[25]  李幼升,et al.  Ph , 1989 .

[26]  Richard O. Duda,et al.  Subjective bayesian methods for rule-based inference systems , 1976, AFIPS '76.

[27]  Richard M. Friedberg,et al.  A Learning Machine: Part I , 1958, IBM J. Res. Dev..

[28]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[29]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[30]  Judea Pearl,et al.  Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach , 1982, AAAI.

[31]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[32]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[33]  Philippe Smets,et al.  The degree of belief in a fuzzy event , 1981, Inf. Sci..

[34]  David L. Dilcher,et al.  The fossil record , 1992 .

[35]  L. Zheng,et al.  A practical guide to tune of proportional and integral (PI) like fuzzy controllers , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[36]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[37]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[38]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[39]  Jian-Xin Xu,et al.  Why is fuzzy control robust? , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[40]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[41]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[42]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[43]  Piero P. Bonissone,et al.  Soft computing: the convergence of emerging reasoning technologies , 1997, Soft Comput..

[44]  R. Jager,et al.  Interpolation issues in Sugeno-Takagi reasoning , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[45]  Kenneth D. Forbus.  Qualitative Reasoning about Physical Processes , 1981, IJCAI.

[46]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[47]  Alex Fraser,et al.  Simulation of Genetic Systems by Automatic Digital Computers I. Introduction , 1957 .

[48]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[49]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[50]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[51]  Francisco Herrera,et al.  A CLASSIFIED REVIEW ON THE COMBINATION FUZZY LOGIC–GENETIC ALGORITHMS BIBLIOGRAPHY: 1989–1995 , 1997 .

[52]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[53]  L. Zadeh.  Probability measures of Fuzzy events , 1968 .

[54]  Piero P. Bonissone,et al.  Hybrid soft computing systems: industrial and commercial applications , 1999, Proc. IEEE.

[55]  Benjamin Kuipers,et al.  Commonsense Reasoning about Causality: Deriving Behavior from Structure , 1984, Artif. Intell..

[56]  Judea Pearl,et al.  A Computational Model for Causal and Diagnostic Reasoning in Inference Systems , 1983, IJCAI.

[57]  Arthur C. Sanderson,et al.  Fuzzy logic controlled genetic algorithms versus tuned genetic algorithms: an agile manufacturing application , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[58]  N. Rescher.  Many Valued Logic , 1969 .

[59]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[60]  M. Black.  Vagueness. An Exercise in Logical Analysis , 1937, Philosophy of Science.