LEADER 07299nam 22007815 450 001 9910970542603321 005 20250801082546.0 010 $a3-642-61068-4 024 7 $a10.1007/978-3-642-61068-4 035 $a(CKB)1000000000016918 035 $a(SSID)ssj0000914835 035 $a(PQKBManifestationID)11466078 035 $a(PQKBTitleCode)TC0000914835 035 $a(PQKBWorkID)10866320 035 $a(PQKB)11329529 035 $a(DE-He213)978-3-642-61068-4 035 $a(MiAaPQ)EBC3093727 035 $a(EXLCZ)991000000000016918 100 $a20110907d1996 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aNeural Networks $eA Systematic Introduction /$fby Raul Rojas 205 $a1st ed. 1996. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1996. 215 $a1 online resource (XX, 502 p. 154 illus.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-60505-3 320 $aIncludes bibliographical references and index. 327 $a1. The Biological Paradigm -- 1.1 Neural computation -- 1.2 Networks of neurons -- 1.3 Artificial neural networks -- 1.4 Historical and bibliographical remarks -- 2. Threshold Logic -- 2.1 Networks of functions -- 2.2 Synthesis of Boolean functions -- 2.3 Equivalent networks -- 2.4 Recurrent networks -- 2.5 Harmonic analysis of logical functions -- 2.6 Historical and bibliographical remarks -- 3.Weighted Networks ? The Perceptron -- 3.1 Perceptrons and parallel processing -- 3.2 Implementation of logical functions -- 3.3 Linearly separable functions -- 3.4 Applications and biological analogy -- 3.5 Historical and bibliographical remarks -- 4. Perceptron Learning -- 4.1 Learning algorithms for neural networks -- 4.2 Algorithmic learning -- 4.3 Linear programming -- 4.4 Historical and bibliographical remarks -- 5. Unsupervised Learning and Clustering Algorithms -- 5.1 Competitive learning -- 5.2 Convergence analysis -- 5.3 Principal component analysis -- 5.4 Some applications -- 5.5 Historical and bibliographical remarks -- 6. One and Two Layered Networks -- 6.1 Structure and geometric visualization -- 6.2 Counting regions in input and weight space -- 6.3 Regions for two layered networks -- 6.4 Historical and bibliographical remarks -- 7. The Backpropagation Algorithm -- 7.1 Learning as gradient descent -- 7.2 General feed-forward networks -- 7.3 The case of layered networks -- 7.4 Recurrent networks -- 7.5 Historical and bibliographical remarks -- 8. Fast Learning Algorithms -- 8.1 Introduction ? classical backpropagation -- 8.2 Some simple improvements to backpropagation -- 8.3 Adaptive step algorithms -- 8.4 Second-order algorithms -- 8.5 Relaxation methods -- 8.6 Historical and bibliographical remarks -- 9. Statistics and Neural Networks -- 9.1 Linear and nonlinear regression -- 9.2 Multiple regression -- 9.3Classification networks -- 9.4 Historical and bibliographical remarks -- 10. The Complexity of Learning -- 10.1 Network functions -- 10.2 Function approximation -- 10.3 Complexity of learning problems -- 10.4 Historical and bibliographical remarks -- 11. Fuzzy Logic -- 11.1 Fuzzy sets and fuzzy logic -- 11.2 Fuzzy inferences -- 11.3 Control with fuzzy logic -- 11.4 Historical and bibliographical remarks -- 12. Associative Networks -- 12.1 Associative pattern recognition -- 12.2 Associative learning -- 12.3 The capacity problem -- 12.4 The pseudoinverse -- 12.5 Historical and bibliographical remarks -- 13. The Hopfield Model -- 13.1 Synchronous and asynchronous networks -- 13.2 Definition of Hopfield networks -- 13.3 Converge to stable states -- 13.4 Equivalence of Hopfield and perceptron learning -- 13.5 Parallel combinatorics -- 13.6 Implementation of Hopfield networks -- 13.7 Historical and bibliographical remarks -- 14. Stochastic Networks -- 14.1 Variations of the Hopfield model -- 14.2 Stochastic systems -- 14.3 Learning algorithms and applications -- 14.4 Historical and bibliographical remarks -- 15. Kohonen Networks -- 15.1 Self-organization -- 15.2 Kohonen?s model -- 15.3 Analysis of convergence -- 15.4 Applications -- 15.5 Historical and bibliographical remarks -- 16. Modular Neural Networks -- 16.1 Constructive algorithms for modular networks -- 16.2 Hybrid networks -- 16.3 Historical and bibliographical remarks -- 17. Genetic Algorithms -- 17.1 Coding and operators -- 17.2 Properties of genetic algorithms -- 17.3 Neural networks and genetic algorithms -- 17.4 Historical and bibliographical remarks -- 18. Hardware for Neural Networks -- 18.1 Taxonomy of neural hardware -- 18.2 Analog neural networks -- 18.3 Digital networks -- 18.4 Innovative computer architectures -- 18.5 Historical and bibliographicalremarks. 330 $aArtificial neural networks are an alternative computational paradigm with roots in neurobiology which has attracted increasing interest in recent years. This book is a comprehensive introduction to the topic that stresses the systematic development of the underlying theory. Starting from simple threshold elements, more advanced topics are introduced, such as multilayer networks, efficient learning methods, recurrent networks, and self-organization. The various branches of neural network theory are interrelated closely and quite often unexpectedly, so the chapters treat the underlying connection between neural models and offer a unified view of the current state of research in the field. The book has been written for anyone interested in understanding artificial neural networks or in learning more about them. The only mathematical tools needed are those learned during the first two years at university. The text contains more than 300 figures to stimulate the intuition of the reader and to illustrate the kinds of computation performed by neural networks. Material from the book has been used successfully for courses in Germany, Austria and the United States. 606 $aArtificial intelligence 606 $aComputer simulation 606 $aPattern recognition systems 606 $aMicroprocessors 606 $aComputer architecture 606 $aComputer science 606 $aBioinformatics 606 $aArtificial Intelligence 606 $aComputer Modelling 606 $aAutomated Pattern Recognition 606 $aProcessor Architectures 606 $aTheory of Computation 606 $aComputational and Systems Biology 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aPattern recognition systems. 615 0$aMicroprocessors. 615 0$aComputer architecture. 615 0$aComputer science. 615 0$aBioinformatics. 615 14$aArtificial Intelligence. 615 24$aComputer Modelling. 615 24$aAutomated Pattern Recognition. 615 24$aProcessor Architectures. 615 24$aTheory of Computation. 615 24$aComputational and Systems Biology. 676 $a006.3 700 $aRojas$b Raul$4aut$4http://id.loc.gov/vocabulary/relators/aut$061314 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910970542603321 996 $aNeural Networks$9375174 997 $aUNINA