Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief]

From the Publisher: "Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems." -- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

[1]  J. McDermott,et al.  Production system conflict resolution strategies , 1977, SGAR.

[2]  H. Carter.  Fuzzy Sets and Systems — Theory and Applications , 1982 .

[3]  Adi R. Bulsara.  Bistability, noise, and information processing in sensory neurons , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[4]  Joseph Montanarella,et al.  Artificial Intelligence : A Knowledge-Based Approach , 1996 .

[5]  J. Schwinn.  A hybrid approach for knowledge representation and reasoning , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

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

[7]  A. V. Metcalfe,et al.  Statistics in engineering : a practical approach , 1996 .

[8]  B. H. Gwee,et al.  A GA paradigm for learning fuzzy rules , 1996, Fuzzy Sets Syst..

[9]  Y. Katoh,et al.  Gradual Rules in a Decision Support System for Foreign Exchange Trading , 1992 .

[10]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[11]  Shun-ichi Amari,et al.  Mathematical foundations of neurocomputing , 1990, Proc. IEEE.

[12]  Takeshi Yamakawa,et al.  A fuzzy inference engine in nonlinear analog mode and its application to a fuzzy logic control , 1993, IEEE Trans. Neural Networks.

[13]  Yoichi Hayashi,et al.  A Neural Expert System with Automated Extraction of Fuzzy If-Then Rules , 1990, NIPS.

[14]  R. Hecht-Nielsen.  Counterpropagation networks. , 1987, Applied optics.

[15]  K. Kaneko.  Pattern dynamics in spatiotemporal chaos: Pattern selection, diffusion of defect and pattern competition intermettency , 1989 .

[16]  E. R. Khan.  NeuFuz: an intelligent combination of fuzzy logic with neural nets , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[17]  Kiyohiro Shikano,et al.  Modularity and scaling in large phonemic neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[18]  Naoki Kimura,et al.  An emotion-processing system based on fuzzy inference and its subjective observations , 1994, Int. J. Approx. Reason..

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

[20]  H. de Garis.  'COMPO' conceptual clustering with connectionist competitive learning , 1989 .

[21]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[22]  Morris W. Firebaugh,et al.  Artificial intelligence: a knowledge-based approach , 1988 .

[23]  Li-Min Fu.  Building expert systems on neural architecture , 1989 .

[24]  J. G. Taylor,et al.  From Wetware to Hardware: Reverse Engineering Using Probabilistic RAMs , 1992 .

[25]  Alianna J. Maren,et al.  Hybrid and complex networks , 1990 .

[26]  Jaap Hoepelman,et al.  Artificial intelligence and expert systems development: David W. Rolston McGraw-Hill, USA (1988) £12.95, ISBN 0-07-053614-7, 278 pp , 1989, Knowl. Based Syst..

[27]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[28]  Kurt Hornik,et al.  Some new results on neural network approximation , 1993, Neural Networks.

[29]  Paul Smolensky,et al.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..

[30]  Madan M. Gupta.  Fuzzy logic and neural networks , 1992, [Proceedings 1992] IEEE International Conference on Systems Engineering.

[31]  Nikola K. Kasabov.  Hybrid Connectionist Fuzzy Production System: Towards Building Comprehensive AI , 1995, Intell. Autom. Soft Comput..

[32]  S. Amari,et al.  Competition and Cooperation in Neural Nets , 1982 .

[33]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[34]  L. N. Kanal,et al.  Uncertainty in Artificial Intelligence 5 , 1990 .

[35]  Geoffrey E. Hinton.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .

[36]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[37]  K. Kaneko.  Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements , 1990 .

[38]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[39]  C. Forgy,et al.  PRODUCTION SYSTEM CONFLICT RESOLUTION STRATEGIES1 , 1978 .

[40]  Patrick Gallinari,et al.  Multilayer perceptrons and data analysis , 1988, IEEE 1988 International Conference on Neural Networks.

[41]  Shun-ichi Amari,et al.  Characteristics of sparsely encoded associative memory , 1989, Neural Networks.

[42]  Shun-Ichi Amari,et al.  Mathematical methods of neurocomputing , 1993 .

[44]  Stephen Grossberg,et al.  ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures , 1990, Neural Networks.

[45]  Geoffrey E. Hinton,et al.  Schemata and Sequential Thought Processes in PDP Models , 1986 .

[46]  Abraham Kandel,et al.  Fuzzy relational data bases : a key to expert systems , 1984 .

[47]  Nikola Kasabov,et al.  Learning Fuzzy Production Rules For Approximate Reasoning In Connectionist Production Systems , 1993 .

[48]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[49]  J. J. Hopfield,et al.  ‘Unlearning’ has a stabilizing effect in collective memories , 1983, Nature.

[50]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[51]  S. Grossberg.  On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks , 1969 .

[52]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[53]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[54]  Didier Dubois,et al.  A review of fuzzy set aggregation connectives , 1985, Inf. Sci..

[55]  Lotfi A. Zadeh,et al.  A Theory of Approximate Reasoning , 1979 .

[56]  Nikola K. Kasabov,et al.  Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems , 1996, Fuzzy Sets Syst..

[57]  Patrick Naïm,et al.  Applications of Neural Networks , 1991 .

[58]  P. Kokotovic,et al.  Nonlinear control via approximate input-output linearization: the ball and beam example , 1992 .

[59]  Nikola K. Kasabov.  Connectionist Fuzzy Production Systems , 1993, Fuzzy Logic in Artificial Intelligence.

[60]  Michael I. Jordan.  Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .

[61]  Y. Abu-Mostafa.  Machines that Learn from Hints , 1995 .

[62]  R. Katayama,et al.  Self generating radial basis function as neuro-fuzzy model and its application to nonlinear prediction of chaotic time series , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[63]  K. Aihara,et al.  Chaotic neural networks , 1990 .

[64]  Nikola K. Kasabov.  Hybrid Connectionist Rule-Based Systems , 1990, AIMSA.

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

[66]  James Gleick,et al.  Chaos, Making a New Science , 1987 .

[67]  Anna Hart,et al.  Knowledge acquisition for expert systems , 1988 .

[68]  Henry D. I. Abarbanel,et al.  Analysis of Observed Chaotic Data , 1995 .

[69]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[70]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[71]  W. Ditto,et al.  Controlling chaos in the brain , 1994, Nature.

[72]  Michio Sugeno,et al.  Fuzzy systems theory and its applications , 1991 .

[73]  Sukhdev Khebbal,et al.  Intelligent Hybrid Systems , 1994 .

[74]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[75]  George F. Luger,et al.  Artificial Intelligence and the Design of Expert Systems , 1990 .

[76]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[77]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[78]  Frank C. Hoppensteadt,et al.  An introduction to the mathematics of neurons , 1986 .

[79]  Lokendra Shastri,et al.  The Relevance of Connectionism to AI: A Representation and Reasoning Perspective , 1989 .

[80]  Zenon W. Pylyshyn,et al.  Connectionism and cognitive architecture , 1993 .

[81]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[82]  Stephen Grossberg,et al.  Studies of mind and brain , 1982 .

[83]  Giovanni Guida,et al.  Topics in Expert System Design: Methodologies and Tools , 1989 .

[84]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[85]  A. N. Burkitt.  External stimuli in optimised attractor neural networks for sparsely coded patterns , 1993 .

[86]  Albert Nigrin,et al.  Neural networks for pattern recognition , 1993 .

[87]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[88]  Michael T. Manry,et al.  Neural networks: Algorithms, applications, and programming techniques: By James A. Freeman and David M. Skapura, Addison-Wesley Publishing, Reading, MA, ISBN 0-201-51376-5 , 1994 .

[89]  W. Freeman,et al.  Spatial EEG patterns, non-linear dynamics and perception: the neo-sherringtonian view , 1985, Brain Research Reviews.

[90]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[91]  Jf Baldwin,et al.  An Introduction to Fuzzy Logic Applications in Intelligent Systems , 1992 .

[92]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[93]  Gary Riley,et al.  Expert Systems , 1989 .

[94]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

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

[96]  Teuvo Kohonen,et al.  Physiological interpretation of the Self-Organizing Map algorithm , 1993, Neural Networks.

[97]  Christopher L. Scofield,et al.  Neural networks and speech processing , 1991, The Kluwer international series in engineering and computer science.

[98]  R. Eckmiller,et al.  Information processing in biology-inspired pulse coded neural networks , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

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

[100]  Martin Brown,et al.  Neurofuzzy adaptive modelling and control , 1994 .

[101]  T. Yamakawa.  Fuzzy logic hardware systems , 1989, Symposium 1989 on VLSI Circuits.

[102]  Terrence J. Sejnowski,et al.  The Computational Brain , 1996, Artif. Intell..

[103]  T. Yamakawa,et al.  Fuzzy rule-based simple interpolation algorithm for discrete signal , 1993 .

[104]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[105]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[106]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[107]  David W. Rolston.  Principles of Artificial Intelligence and Expert Systems Development , 1988 .

[108]  Nikola IL Kasabov.  Adaptable neuro production systems , 1996, Neurocomputing.

[109]  Terrence J. Sejnowski,et al.  Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..

[110]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[111]  Lokendra Shastri,et al.  Rules and Variables in Neural Nets , 1991, Neural Computation.

[112]  Yoshiyasu Takefuji,et al.  Implementing fuzzy rule-based systems on silicon chips , 1990, IEEE Expert.

[113]  Nikola K. Kasabov,et al.  Prognostic Expert Systems on a Hybrid Connectionist Environment , 1992, AIMSA.

[114]  P. C. Treleaven,et al.  A framework for hybrid intelligent systems , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[115]  Lotfi A. Zadeh,et al.  PRUF—a meaning representation language for natural languages , 1978 .

[116]  Ron Sun,et al.  Computational Architectures Integrating Neural And Symbolic Processes , 1994 .

[117]  A. C. Gimson,et al.  An introduction to the pronunciation of English , 1991 .

[118]  Brian Schott,et al.  Alternative Logics for Approximate Reasoning in Expert Systems: A Comparative Study , 1985, Int. J. Man Mach. Stud..

[119]  Shyi-Mig Chen,et al.  A new approach to handling fuzzy decision-making problems , 1988, [1988] Proceedings. The Eighteenth International Symposium on Multiple-Valued Logic.

[120]  P J Webros.  BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[121]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[122]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[123]  Masumi Ishikawa,et al.  Neural networks approach to rule extraction , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[124]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[125]  Nikola Kasabov,et al.  Phoneme Recognition with Hierarchical Self Organised Neural Networks and Fuzzy Systems - A Case Study , 1994 .

[126]  James R. Koehler,et al.  Statistics in Engineering: A Practical Approach , 1996 .

[127]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[128]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[129]  Yoshiki Uchikawa,et al.  An efficient finding of fuzzy rules using a new approach to genetic based machine learning , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[130]  Robert J. Schalkoff,et al.  Artificial Intelligence: An Engineering Approach , 1990 .

[131]  F C Hoppensteadt,et al.  Intermittent chaos, self-organization, and learning from synchronous synaptic activity in model neuron networks. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[132]  Charles P. Dolan,et al.  Tensor Product Production System: a Modular Architecture and Representation , 1989 .

[133]  V. Tikhomirov.  On the Representation of Continuous Functions of Several Variables as Superpositions of Continuous Functions of one Variable and Addition , 1991 .

[134]  Valluru Rao,et al.  C++ neural networks and fuzzy logic , 1993 .

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

[136]  Cynthia A. Berg.  Cognitive development: An information-processing approach. , 1987 .

[137]  Igor Aleksander,et al.  Neural computing architectures: the design of brain-like machines , 1989 .

[138]  H. Zimmermann,et al.  Comparison of fuzzy reasoning methods , 1982 .

[139]  R. Haber,et al.  Visual Perception , 2018, Encyclopedia of Database Systems.

[140]  John H. Andreae,et al.  The chaotic self-organizing map , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[141]  Lotfi A. Zadeh,et al.  Similarity relations and fuzzy orderings , 1971, Inf. Sci..

[142]  M. Gupta,et al.  FUZZY INFORMATION AND DECISION PROCESSES , 1981 .

[143]  J. McCauley.  Chaos, dynamics, and fractals : an algorithmic approach to deterministic chaos , 1993 .

[144]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[145]  Paul Bourgine,et al.  Rule extraction and validity domain on a multilayer neural network , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[146]  Nikola K. Kasabov.  Hybrid Connectionist Fuzzy Systems for Speech Recognition and the Use of Connectionist Production Systems , 1994, IEEE/Nagoya-University World Wisepersons Workshop.

[147]  Igor Aleksander,et al.  Neurons and Symbols: The Stuff That Mind Is Made of , 1993 .

[148]  Wilfrid S. Kendall,et al.  Networks and Chaos - Statistical and Probabilistic Aspects , 1993 .

[149]  Masumi Ishikawa,et al.  Prediction of time series by a structural learning of neural networks , 1996, Fuzzy Sets Syst..

[150]  I︠a︡. Z. T︠S︡ypkin,et al.  Foundations of the theory of learning systems , 1973 .

[151]  Bart Kosko,et al.  Fuzzy entropy and conditioning , 1986, Inf. Sci..

[152]  Dirk Van Compernolle.  Speech recognition with neural networks and hybrid systems , 1994 .

[153]  L. A. Zedeh.  Knowledge representation in fuzzy logic , 1989 .

[154]  E. Rolls,et al.  Neural networks in the brain involved in memory and recall , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

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

[156]  A. Bulsara,et al.  Stochastic resonance in a single neuron model: theory and analog simulation. , 1991, Journal of theoretical biology.

[157]  Steven Pinker,et al.  On language and connectionism , 1988 .

[158]  Bernd Freisleben.  A neural network that learns to play five-in-a-row , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[159]  Bart Kosko,et al.  Neural networks for signal processing , 1992 .

[160]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[161]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[162]  Michio Sugeno,et al.  An introductory survey of fuzzy control , 1985, Inf. Sci..

[163]  Yaser S. Abu-Mostafa,et al.  A Method for Learning From Hints , 1992, NIPS.

[164]  Lotfi A. Zadeh,et al.  MAKING COMPUTERS THINK LIKE PEOPLE , 1984 .

[165]  Kai-Fu Lee,et al.  Automatic Speech Recognition , 1989 .

[166]  Catherine I. Watson,et al.  Intelligent human computer interfaces and the case study of building English-to-Maori talking dictionary , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[167]  Lokendra Shastri.  A Connectionist Approach to Knowledge Representation and Limited Inference , 1988 .

[168]  Anca L. Ralescu,et al.  Some issues in fuzzy and linguistic modeling , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[169]  Yoshiki Uchikawa,et al.  A fuzzy neural network for identifying changes of degrees of attention in a multi-attribute decision making process , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[170]  J. Barhen,et al.  'Chaotic relaxation' in concurrently asynchronous neurodynamics , 1989, International 1989 Joint Conference on Neural Networks.

[171]  Jon Doyle,et al.  A Truth Maintenance System , 1979, Artif. Intell..

[172]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[173]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[174]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[175]  Zengo Furukawa,et al.  A General Framework for , 1991 .

[176]  Lawrence O. Hall,et al.  Decision making on creditworthiness, using a fuzzy connectionist model , 1992 .

[177]  Bernard Angéniol,et al.  Self-organizing feature maps and the travelling salesman problem , 1988, Neural Networks.

[178]  Sankar K. Pal,et al.  Fuzzy multi-layer perceptron, inferencing and rule generation , 1995, IEEE Trans. Neural Networks.

[179]  J. G. Taylor.  New Developments in Neural Computing , 1989 .

[180]  M. Zehana,et al.  A Connectionist Approach for a Knowledge Based Image Interpretation System , 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.

[181]  Donald A. Waterman,et al.  Pattern-Directed Inference Systems , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[182]  Masaki Togai,et al.  Expert System on a Chip: An Engine for Real-Time Approximate Reasoning , 1986, IEEE Expert.

[183]  M. Arbib.  Brains, Machines, and Mathematics , 1987, Springer US.

[184]  Shun-ichi Amari,et al.  Differential-geometrical methods in statistics , 1985 .

[185]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[186]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[187]  Larry R. Medsker,et al.  Hybrid Intelligent Systems , 1995, Springer US.

[188]  W. Freeman.  The physiology of perception. , 1991, Scientific American.

[189]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[190]  James A. Hendler.  Editorial: On The Need for Hybrid Systems , 1989 .

[191]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

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

[193]  D. Ralescu,et al.  Fuzzy random variables revisited , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[194]  Fatemeh Zahedi,et al.  Intelligent systems for business , 1992 .

[195]  Takayuki Ito,et al.  Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

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

[197]  Richard P. Lippmann,et al.  Review of Neural Networks for Speech Recognition , 1989, Neural Computation.

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

[199]  Nikola Kasabov,et al.  A Connectionist Production System with Partial Match and its Use for Approximate Reasoning , 1993 .

[200]  Nikola K. Kasabov,et al.  Neural Networks and Logic Programming - a Hybrid Model and its Applicability to Building Expert Systems , 1992, European Conference on Artificial Intelligence.

[201]  Eberhard Schöneburg,et al.  Stock price prediction using neural networks : A project report , 2003 .

[202]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[203]  Thomas Jackson,et al.  Neural Computing - An Introduction , 1990 .

[204]  Nikola K. Kasabov,et al.  Hybrid systems for prediction-a case study of predicting effluent flow to a sewage plant , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[205]  Yiannis Aloimonos,et al.  Artificial intelligence - theory and practice , 1995 .

[206]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[207]  D. Casasent,et al.  Optical neural networks for image analysis: imaging spectroscopy and production systems , 1988, IEEE 1988 International Conference on Neural Networks.

[208]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[209]  Geoffrey E. Hinton.  Connectionist Symbol Processing , 1991 .

[210]  G Kohers.  The use of modular neural networks in time series forecasting. , 1992 .

[211]  Elisabetta Binaghi,et al.  Empirical learning for fuzzy knowledge acquisition , 1992 .

[212]  J. Hopfield.  Neurons withgraded response havecollective computational properties likethoseoftwo-state neurons , 1984 .

[213]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[214]  Frank J. Owens.  Signal processing of speech , 1993 .

[215]  Erkki Oja,et al.  Principal components, minor components, and linear neural networks , 1992, Neural Networks.

[216]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[217]  S. Renals,et al.  Phoneme classification experiments using radial basis functions , 1989, International 1989 Joint Conference on Neural Networks.

[218]  Terry Elliott,et al.  Instance-Based and Generalization-Based Learning Procedures Applied To Solving Integration Problems. , 1991 .

[219]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[220]  James A. Hendler,et al.  Integrating Neural Network and Expert Reasoning: An Example , 1991 .

[221]  C. J. Wang,et al.  NCLIPS-a platform for implementing hybrid expert systems , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[222]  Edgar E. Peters.  Chaos and order in the capital markets , 1991 .

[223]  J. Ozawa,et al.  Answering to conceptual queries , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[224]  Kaoru Hirota.  Fuzzy and Image Pattern Recognition , 1993 .

[225]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

[226]  Seymour Geisser,et al.  The Predictive Sample Reuse Method with Applications , 1975 .

[227]  Françoise Fogelman Soulié,et al.  Neural Network Architectures for Pattern Recognition , 1994 .

[228]  Ron Sun,et al.  On Variable Binding in Connectionist Networks , 1992 .

[229]  James A. Anderson,et al.  Cognitive and psychological computation with neural models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[230]  Andy Clark,et al.  Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing , 1989 .

[231]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[232]  Kishan G. Mehrotra,et al.  Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.

[233]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[234]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[235]  Sylvie Thiria,et al.  Cooperation of neural nets for robust classification , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[236]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[237]  Robert Hecht-Nielsen,et al.  Applications of counterpropagation networks , 1988, Neural Networks.

[238]  B. Kosko,et al.  Feedback stability and unsupervised learning , 1988, IEEE 1988 International Conference on Neural Networks.

[239]  Tom Michael Mitchell,et al.  Explanation-based generalization: A unifying view , 1986 .

[240]  John A. Barnden,et al.  Encoding techniques for complex information structures in connectionist systems , 1991 .

[241]  T. Vaga,et al.  The Coherent Market Hypothesis , 1990 .

[242]  Michael G. Dyer,et al.  High-level Inferencing in a Connectionist Network , 1989 .

[243]  Roger M. Cooke,et al.  Probabilistic Reasoning in Expert Systems Reconstructed in Probability Semantics , 1986, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.

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

[245]  Madan M. Gupta,et al.  Approximate reasoning in expert systems , 1985 .

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

[247]  H. L. Cather.  Techniques in Computational Learning—an Introduction , 1993 .

[248]  Susan I. Hruska,et al.  Hybrid learning in expert networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[249]  Harry A. C. Eaton,et al.  Learning coefficient dependence on training set size , 1992, Neural Networks.

[250]  James A. Reggia,et al.  A connectionist model for diagnostic problem solving , 1989, IEEE Trans. Syst. Man Cybern..

[251]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[252]  Lotfi A. Zadeh,et al.  Knowledge Representation in Fuzzy Logic , 1996, IEEE Trans. Knowl. Data Eng..

[253]  Martin Anthony,et al.  Computational learning theory: an introduction , 1992 .

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

[255]  Bart Kosko,et al.  Unsupervised learning in noise , 1990, International 1989 Joint Conference on Neural Networks.

[256]  Perry J. Kaufman,et al.  The new commodity trading systems and methods , 1987 .

[257]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..