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Neural Advances in Processing Nonlinear Dynamic Signals

This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as;

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Nonlinear Biomedical Signal Processing

advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive;

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Nonlinear Biomedical Signal Processing, Volume 1

newest theories and applications of fuzzy logic, neural networks, and algorithms in biomedicine. Nonlinear Biomedical Signal Processing, Volume I;

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Handbook of Neural Network Signal Processing

, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together;

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Nonlinear Dynamical Systems

nonlinear input-output maps The development of robust controllers and filters The capability of neural networks to approximate functions and dynamic;

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Supervised Learning with Complex-valued Neural Networks

varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial;

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Biomedical Signal Processing

This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems;

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Nonlinear Dynamics in Computational Neuroscience

the nervous system. The contents build on the workshop Nonlinear Dynamics in Computational Neuroscience: from Physics and Biology to ICT;

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Intelligence For Nonlinear Dynamics And Synchronization

complex nonlinear dynamic systems has become indubitable, as attested by a large number of studies reporting on the successful application of CI;

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Machine Intelligence and Signal Processing

Processing (MISP 2019), held at the Indian Institute of Technology, Allahabad, India, on September 7-10, 2019. The book covers the latest advances;

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Nonlinear Signal and Image Processing

of increasingly sophisticated applications. Advances in computing performance and nonlinear theory are making nonlinear techniques not only viable, but;

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Biomedical Signal Processing and Artificial Intelligence in Healthcare

Learning, Neural Networks, and the implications of the expansion of artificial intelligence is covered. Biomedical Images are also introduced and;

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Sensory Neural Networks

interaction that produces significant global properties. This book discusses those sensory neural networks influenced by nonlinear lateral inhibition;

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Revival: Sensory Neural Networks (1991)

interaction that produces significant global properties. This book discusses those sensory neural networks influenced by nonlinear lateral inhibition;

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Signal Processing in Auditory Neuroscience

of different sounds manifest in neural signals and how to tease-apart their different influences. It includes EEG/MEG as additional variables to be;

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Analytical Evaluation of Nonlinear Distortion Effects on Multicarrier Signals

nonlinear distortion effects. Analytical Evaluation of Nonlinear Distortion Effects on Multicarrier Signals details a unified approach to well-known;

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Analytical Evaluation of Nonlinear Distortion Effects on Multicarrier Signals

nonlinear distortion effects. Analytical Evaluation of Nonlinear Distortion Effects on Multicarrier Signals details a unified approach to well-known;

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Wavelets in Neuroscience

at different levels: from the microscopic dynamics of individual cells to the macroscopic behavior of large neural networks. It also;

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Intelligent Signal Processing

, Hamilton, Ontario, Canada, where he serves as university professor. His research interests include nonlinear dynamics, neural networks and adaptive;

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Diffuse Algorithms for Neural and Neuro-Fuzzy Networks

Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new;

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Mathematical Tools In Signal Processing With C++ And Java Simulations

systems, encompassing standard mathematical tools as well as newer techniques such as wavelets and neural networks. C++ and Java implementations;

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Applications of Artificial Neural Networks for Nonlinear Data

deficient. Applications of Artificial Neural Networks for Nonlinear Data is a collection of innovative research on the contemporary nature;

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Recurrent Neural Networks for Prediction

presenting the latest research work the authors demonstrate how real--time recurrent neural networks (RNNs) can be implemented to expand the range;

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Neural Network Control of Nonlinear Discrete-Time Systems

. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the;

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Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals

classification method, the Adaline Neural Network is used and compared with other common classifiers. Theory enhancement, original applications and;

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Adaptive Processing of Brain Signals

different modality neuroimaging systems are discussed. These include constrained nonlinear signal processing techniques which incorporate sparsity;

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Cellular Neural Networks, Multi-scroll Chaos And Synchronization

For engineering applications that are based on nonlinear phenomena, novel information processing systems require new methodologies and;

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