Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms (1) (IEEE Press Series in Biome
For the first time, eleven experts in the fields of signal processing and biomedical engineering have contributed to an edition on the newest theories and applications of fuzzy logic, neural networks, and algorithms in biomedicine. Nonlinear Biomedical Signal Processing, Volume I provides comprehensive coverage of nonlinear signal processing techniques. In the last decade, theoretical developments in the concept of fuzzy logic have led to several new approaches to neural networks. This...
Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real--time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. ? Analyses the...
Nonlinear Biomedical Signal Processing, Volume 2: Dynamic Analysis and Modeling (IEEE Press Series on Biomedical Enginee
Featuring current contributions by experts in signal processing and biomedical engineering, this book introduces the concepts, recent advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive coverage of nonlinear signal and image processing techniques. Nonlinear Biomedical Signal Processing: Volume II combines analytical and biological expertise in the original mathematical simulation and modeling of...
Nonlinear and Adaptive Control Design (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communicatio
Using a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping. Describes basic tools for nonadaptive backstepping design with state and output feedbacks.
Signal and Image Processing with Neural Networks: A C++ Sourcebook
Demonstrates how neural networks can be used to aid in the solution of digital signal processing (DSP) or imaging problems. A large section is devoted to the design and training of complex-domain multiple-layer feedforward networks (MLFNs)—all essential equations are presented and justified. Reviews the most popular signal- and image-processing algorithms, emphasizing those that are particularly suitable for union to complex-domain neural networks. Features a wide variety of problems for...
Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.
Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms (1) (IEEE Press Series in Biome
For the first time, eleven experts in the fields of signal processing and biomedical engineering have contributed to an edition on the newest theories and applications of fuzzy logic, neural networks, and algorithms in biomedicine. Nonlinear Biomedical Signal Processing, Volume I provides comprehensive coverage of nonlinear signal processing techniques. In the last decade, theoretical developments in the concept of fuzzy logic have led to several new approaches to neural networks. This...
Nonlinear Biomedical Signal Processing, Volume 2: Dynamic Analysis and Modeling (IEEE Press Series on Biomedical Enginee
Featuring current contributions by experts in signal processing and biomedical engineering, this book introduces the concepts, recent advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive coverage of nonlinear signal and image processing techniques. Nonlinear Biomedical Signal Processing: Volume II combines analytical and biological expertise in the original mathematical simulation and modeling of...
Biomedical Signal Processing and Signal Modeling
A biomedical engineering perspective on the theory, methods, and applications of signal processing This book provides a unique framework for understanding signal processing of biomedical signals and what it tells us about signal sources and their behavior in response to perturbation. Using a modeling-based approach, the author shows how to perform signal processing by developing and manipulating a model of the signal source, providing a logical, coherent basis for recognizing signal types...
Neural Networks: A Comprehensive Foundation
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
Nonlinear and Adaptive Control Design (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communicatio
Using a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping. Describes basic tools for nonadaptive backstepping design with state and output feedbacks.
Signal and Image Processing with Neural Networks: A C++ Sourcebook
Demonstrates how neural networks can be used to aid in the solution of digital signal processing (DSP) or imaging problems. A large section is devoted to the design and training of complex-domain multiple-layer feedforward networks (MLFNs)—all essential equations are presented and justified. Reviews the most popular signal- and image-processing algorithms, emphasizing those that are particularly suitable for union to complex-domain neural networks. Features a wide variety of problems for...
