{"product_id":"intelligent-signal-processing-isbn-9780780360105","title":"Intelligent Signal Processing","description":"\"IEEE Press is proud to present the first selected reprint volume devoted to the new field of intelligent signal processing (ISP). ISP differs fundamentally from the classical approach to statistical signal processing in that the input-output behavior of a complex system is modeled by using \"intelligent\" or \"model-free\" techniques, rather than relying on the shortcomings of a mathematical model. Information is extracted from incoming signal and noise data, making few assumptions about the statistical structure of signals and their environment.\u003cbr\u003e \u003cbr\u003e Intelligent Signal Processing explores how ISP tools address the problems of practical neural systems, new signal data, and blind fuzzy approximators. The editors have compiled 20 articles written by prominent researchers covering 15 diverse, practical applications of this nascent topic, exposing the reader to the signal processing power of learning and adaptive systems.\u003cbr\u003e \u003cbr\u003e This essential reference is intended for researchers, professional engineers, and scientists working in statistical signal processing and its applications in various fields such as humanistic intelligence, stochastic resonance, financial markets, optimization, pattern recognition, signal detection, speech processing, and sensor fusion. Intelligent Signal Processing is also invaluable for graduate students and academics with a background in computer science, computer engineering, or electrical engineering.\u003cbr\u003e \u003cbr\u003e About the Editors\u003cbr\u003e \u003cbr\u003e Simon Haykin is the founding director of the Communications Research Laboratory at McMaster University, Hamilton, Ontario, Canada, where he serves as university professor. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on \"Adaptive and Learning Systems for Signal Processing, Communications and Control\" (Publisher) and is both an IEEE Fellow and Fellow of the Royal Society of Canada.\u003cbr\u003e \u003cbr\u003e Bart Kosko is a past director of the University of Southern California's (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Publisher, copyright date) and Fuzzy Thinking (Publisher, copyright date), as well as the novel Nanotime (Publisher, copyright date). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. Currently, he is associate professor of electrical engineering at USC.\"  Preface.\u003cbr\u003e \u003cbr\u003e List of Contributors.\u003cbr\u003e \u003cbr\u003e Humanistic Intelligence: \"Wear Comp\" As a New Framework and Application for Intelligent Signal Processing.\u003cbr\u003e \u003cbr\u003e Adaptive Stochastic Resonance.\u003cbr\u003e \u003cbr\u003e Learning in the Presence of Noise.\u003cbr\u003e \u003cbr\u003e Incorporating Prior Information in Machine Learning by Creating Virtual Examples.\u003cbr\u003e \u003cbr\u003e Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech recognition.\u003cbr\u003e \u003cbr\u003e Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control.\u003cbr\u003e \u003cbr\u003e A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification.\u003cbr\u003e \u003cbr\u003e Semiparametric Support Vector Machines for Nonlinear Model Estimation.\u003cbr\u003e \u003cbr\u003e Gradient-Based Learning Applied to Document Recognition.\u003cbr\u003e \u003cbr\u003e Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method.\u003cbr\u003e \u003cbr\u003e An Approach to Adaptive Classification.\u003cbr\u003e \u003cbr\u003e Reduced-Rank Intelligent Signal Processing with Application to Radar.\u003cbr\u003e \u003cbr\u003e Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem.\u003cbr\u003e \u003cbr\u003e Data Representation Using Mixtures of Principal Components.\u003cbr\u003e \u003cbr\u003e Image Denoising by Sparse Code Shrinkage.\u003cbr\u003e \u003cbr\u003e Index.\u003cbr\u003e \u003cbr\u003e About the Editors.  \u003cb\u003eSimon Haykin\u003c\/b\u003e is University Professor at McMaster University, Hamilton, Ontario, Canada. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on \u003ci\u003eAdaptive and Learning Systems for Signal Processing, Communications and Control\u003c\/i\u003e published by John Wiley \u0026amp; Sons, Inc. He is both an IEEE Fellow and Fellow of the Royal Society of Canada.  \u003cp\u003e\u003cb\u003eBart Kosko\u003c\/b\u003e is a past director of the University of Southern California’s (USC) Signal and Image Processing Institute. He has authored several books, including \u003ci\u003eNeural Networks and Fuzzy Systems\u003c\/i\u003e, \u003ci\u003eNeural Networks for Signal Processing\u003c\/i\u003e (Prentice Hall, 1992) \u003ci\u003eFuzzy Engineering\u003c\/i\u003e (Prentice Hall, 1997) and \u003ci\u003eFuzzy Thinking\u003c\/i\u003e (Hyperion, 1993), as well as the novel Nanotime (Avon Books, 1997) and Heaven in a Chip (Random House, 2000). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. He is a faculty member of electrical engineering at USC.\u003c\/p\u003e IEEE Press is proud to present the first selected reprint volume devoted to the new field of intelligent signal processing (ISP). ISP differs fundamentally from the classical approach to statistical signal processing in that it models the input-out-put behavior of a complex system by using \"intelligent\" or \"model-free\" techniques rather than relying on the shortcomings of a mathematical model. ISP systems extract information from incoming signal and noise data and makes few assumptions about the statistical structure of signals and their environment. Intelligent Signal Processing explores how ISP tools address the problems of practical neural systems, new signal data, and blind fuzzy approximators. The editors have compiled 20 articles written by prominent researchers covering diverse practical applications of this nascent topic, exposing the reader to the signal processing power of learning and adaptive systems. This essential reference is intended for researchers, professional engineers, and scientists working in statistical signal processing and its applications in various fields such as humanistic intelligence, stochastic resonance, financial markets, noise processing optimization, pattern recognition, signal detection, speech processing, and sensor fusion. Intelligent Signal Processing is also invaluable for graduate students and academics with a background in computer science, computer engineering, or electrical engineering.","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989441495269,"sku":"NP9780780360105","price":255.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780780360105.jpg?v=1761784112","url":"https:\/\/k12savings.com\/products\/intelligent-signal-processing-isbn-9780780360105","provider":"K12savings","version":"1.0","type":"link"}