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Neural Networks - Algorithms, Applications,and ... - Csbdu.in

Neural Networks - Algorithms, Applications,and ... - Csbdu.in

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viiiPrefaceprocess<strong>in</strong>g model, we have adhered, as much as possible, to the notation <strong>in</strong>the PDF series. The simulation overview presents a general framework for thesimulations discussed <strong>in</strong> subsequent chapters.Follow<strong>in</strong>g this <strong>in</strong>troductory chapter is a series of chapters, each devoted toa specific network or class of networks. There are n<strong>in</strong>e such chapters:Chapter 2, Adal<strong>in</strong>e <strong>and</strong> Madal<strong>in</strong>eChapter 3, BackpropagationChapter 4, The BAM <strong>and</strong> the Hopfield MemoryChapter 5, Simulated Anneal<strong>in</strong>g: <strong>Networks</strong> discussed <strong>in</strong>clude the Boltzmanncompletion <strong>and</strong> <strong>in</strong>put-output networksChapter 6, The Counterpropagation NetworkChapter 7, Self-Organiz<strong>in</strong>g Maps: <strong>in</strong>cludes the Kohonen topology-preserv<strong>in</strong>gmap <strong>and</strong> the feature-map classifierChapter 8, Adaptive Resonance Theory: <strong>Networks</strong> discussed <strong>in</strong>clude bothART1 <strong>and</strong> ART2Chapter 9, Spatiotemporal Pattern Classification: discusses Hecht-Nielsen'sspatiotemporal networkChapter 10, The NeocognitronEach of these n<strong>in</strong>e chapters conta<strong>in</strong>s a general description of the networkarchitecture <strong>and</strong> a detailed discussion of the theory of operation of the network.Most chapters conta<strong>in</strong> examples of applications that use the particular network.Chapters 2 through 9 <strong>in</strong>clude detailed <strong>in</strong>structions on how to build softwaresimulations of the networks with<strong>in</strong> the general framework given <strong>in</strong> Chapter 1.Exercises based on the material are <strong>in</strong>terspersed throughout the text. A listof suggested programm<strong>in</strong>g exercises <strong>and</strong> projects appears at the end of eachchapter.We have chosen not to <strong>in</strong>clude the usual pseudocode for the neocognitronnetwork described <strong>in</strong> Chapter 10. We believe that the complexity of this networkmakes the neocognitron <strong>in</strong>appropriate as a programm<strong>in</strong>g exercise for students.To compile this survey, we had to borrow ideas from many different sources.We have attempted to give credit to the orig<strong>in</strong>al developers of these networks,but it was impossible to def<strong>in</strong>e a source for every idea <strong>in</strong> the text. To helpalleviate this deficiency, we have <strong>in</strong>cluded a list of suggested read<strong>in</strong>gs after eachchapter. We have not, however, attempted to provide anyth<strong>in</strong>g approach<strong>in</strong>g anexhaustive bibliography for each of the topics that we discuss.Each chapter bibliography conta<strong>in</strong>s a few references to key sources <strong>and</strong> supplementarymaterial <strong>in</strong> support of the chapter. Often, the sources we quote areolder references, rather than the newest research on a particular topic. Many ofthe later research results are easy to f<strong>in</strong>d: S<strong>in</strong>ce 1987, the majority of technicalpapers on ANS-related topics has congregated <strong>in</strong> a few journals <strong>and</strong> conference

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