Handbook of Neural Network Signal ProcessingYu Hen Hu, Jenq-Neng Hwang CRC Press, 3 ต.ค. 2018 - 408 หน้า The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view. The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field. |
เนื้อหา
Radial Basis Functions Andrew D Back | 2-20 |
Committee Machines Volker Tresp | 2-81 |
Dynamic Neural Networks and Optimal Signal Processing Jose C Principe | 2-98 |
Blind Signal Separation and Blind Deconvolution Scott C Douglas | 6-19 |
Neural Networks and Principal Component Analysis Konstantinos I Diamantaras | 6-52 |
Applications of Artificial Neural Networks to Time Series Prediction Yuansong Liao John Moody and Lizhong | 6-88 |
Applications of Artificial Neural Networks ANNs to Speech Processing Shigeru Katagiri | 10 |
Learning and Adaptive Characterization of Visual Contents in Image Retrieval Systems Paisarn Muneesawang HauSan Wong Jose Lay and Ling Guan | 34 |
Applications of Neural Networks to Image Processing Tülay Adali Yue Wang and Huai Li | 95 |
Index | 13-14 |
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คำและวลีที่พบบ่อย
adaptive Advances in Neural analysis applications approach artificial neural networks back-propagation blind deconvolution blind signal separation boosting chapter classification clustering committee machines committee members convergence data set DBNN defined density detection distribution dynamic edge Equation estimation example expert feature space feature vector filter finite Gaussian gradient Hebbian hidden layer hidden units hierarchical hyperplane IEEE IEEE Transactions implementation Information Processing Systems iteration kernel PCA learning algorithm linear mammograms mapping matrix method minimize mixtures modular modules multilayer perceptron Neural Computation Neural Information Processing neuron nonlinear optimal parameters pattern recognition performance pixel principal component problem query radial basis function RBF networks regression relevant retrieval S.Y. Kung Schölkopf Section segmentation sequence series prediction shown in Figure Signal Processing solution source signals speech recognition statistical structure supervised learning support vector machines task techniques theorem training data training patterns unsupervised update values weight vector