Intelligent signal processing / (Record no. 73934)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 08200nam a2201441 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 5265588 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712205659.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 100317t20152001nyuaf ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780470544976 |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.382/2 |
| 245 00 - TITLE STATEMENT | |
| Title | Intelligent signal processing / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (xxi, 573 pages) : |
| 500 ## - GENERAL NOTE | |
| Remark 1 | "A selected reprint volume." |
| 500 ## - GENERAL NOTE | |
| Remark 1 | "IEEE order no. PC5860."--T.p. verso. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface. List of Contributors. Humanistic Intelligence: "Wear Comp" As a New Framework and Application for Intelligent Signal Processing. Adaptive Stochastic Resonance. Learning in the Presence of Noise. Incorporating Prior Information in Machine Learning by Creating Virtual Examples. Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech recognition. Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control. A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification. Semiparametric Support Vector Machines for Nonlinear Model Estimation. Gradient-Based Learning Applied to Document Recognition. Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method. An Approach to Adaptive Classification. Reduced-Rank Intelligent Signal Processing with Application to Radar. Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem. Data Representation Using Mixtures of Principal Components. Image Denoising by Sparse Code Shrinkage. Index. About the Editors. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "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. 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. 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. About the Editors 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. 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.". |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| Subject | Signal processing |
| General subdivision | Digital techniques. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| Subject | Intelligent control systems. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| Subject | Adaptive signal processing. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Haykin, Simon S., |
| 700 1# - AUTHOR 2 | |
| Author 2 | Kosko, Bart. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5265588 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | New York : |
| -- | IEEE Press, |
| -- | c2001. |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [2001] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/21/2015. |
| 695 ## - | |
| -- | Accuracy |
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| -- | Acoustics |
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| -- | Aerodynamics |
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| -- | Annealing |
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| -- | Approximation error |
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| -- | Artificial neural networks |
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| -- | Atmospheric modeling |
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| -- | Biographies |
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| -- | Biological system modeling |
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| -- | Chaos |
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| -- | Character recognition |
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| -- | Classification algorithms |
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| -- | Clutter |
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| -- | Complexity theory |
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| -- | Computers |
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| -- | Cost function |
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| -- | Covariance matrix |
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| -- | Current measurement |
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| -- | Data mining |
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| -- | Delay |
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| -- | Distortion measurement |
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| -- | Encapsulation |
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| -- | Encoding |
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| -- | Entropy |
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| -- | Estimation error |
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| -- | Feature extraction |
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| -- | Feedforward neural networks |
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| -- | Fuzzy systems |
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| -- | Gaussian noise |
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| -- | Handwriting recognition |
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| -- | Hardware |
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| -- | Hidden Markov models |
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| -- | Humans |
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| -- | Indexes |
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| -- | Instruments |
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| -- | Kalman filters |
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| -- | Learning |
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| -- | Learning systems |
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| -- | Machine learning |
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| -- | Maximum likelihood estimation |
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| -- | Mediation |
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| -- | Noise |
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| -- | Noise measurement |
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| -- | Noise reduction |
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| -- | Nonlinear dynamical systems |
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| -- | Optimization |
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| -- | Pixel |
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| -- | Predictive models |
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| -- | Principal component analysis |
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| -- | Privacy |
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| -- | Probabilistic logic |
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| -- | Prototypes |
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| -- | Radar |
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| -- | Radar imaging |
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| -- | Radar signal processing |
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| -- | Random variables |
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| -- | Reflection |
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| -- | Signal detection |
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| -- | Signal processing |
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| -- | Signal processing algorithms |
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| -- | Signal representations |
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| -- | Signal to noise ratio |
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| -- | Speech |
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| -- | Speech recognition |
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| -- | Stochastic resonance |
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| -- | Strontium |
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| -- | Support vector machine classification |
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| -- | Support vector machines |
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| -- | Time series analysis |
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| -- | Training |
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| -- | Trajectory |
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| -- | Vector quantization |
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| -- | Vectors |
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| -- | Viterbi algorithm |
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| -- | Wiener filter |
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