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Static and Dynamic Neural Networks

Provides comprehensive treatment of the theory of both static and dynamic neural networks. Theoretical concepts are illustrated by;

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Zeroing Dynamics, Gradient Dynamics, and Newton Iterations

hardware, and fractals. The authors provide a comprehensive treatment of the theory of both static and dynamic neural networks. Readers will;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations;

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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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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault;

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Connectivity of Communication Networks

: connectivity of large static networks, connectivity of highly dynamic networks, connectivity of small to medium sized networks, and applications;

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Nonlinear Dynamical Systems

different aspects of feedforward neural networks, the book then addresses: Classification problems and the related problem of approximating dynamic;

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Recurrent Neural Networks

This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from;

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Computational Intelligence in Sensor Networks

sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological;

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Learning in Energy-Efficient Neuromorphic Computing - Algorithm and Architecture Co-Design

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning;

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New Developments In Artificial Neural Networks Research

in chromatography and spectroscopy; heterogeneous neural networks and tuning differential evolution for artificial neural networks.;

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Soft Starting of Induction Motors

Starting inrush current and pulsations in the inducedtorque affect the performance of an induction motor.Artificial neural networks (ANNs;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews;

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Evolutionary Machine Learning Techniques

networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions;

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Complex-valued Neural Networks

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are;

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Focus on Artificial Neural Networks

artificial neural networks in environmental sciences and chemical engineering; application of artificial neural networks in the development;

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Applied Neural Networks and Soft Computing

Applied Neural Networks and Soft Computing examines the relation between neural networks and soft computing. Neural network is a system;

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Temporal Networks

between nodes happen. Many studies of the last decade examine how the static network structure affect dynamic systems on the network. In this;

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Complex-Valued Neural Networks

This book covers the hot topics and applications surrounding complex-valued neural networks. It demonstrates advanced theories with a wide;

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Simulating Neural Networks with Mathematica

This book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment. Readers;

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Information-Theoretic Aspects of Neural Networks

this burgeoning discipline, covering topics such as: Shannon information and information dynamics neural complexity as an information;

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Blind Equalization in Neural Networks

The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms;

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Fuzzy and Neural Approaches in Engineering

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. This definitive book presents the;

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Neural Networks with R

with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial;

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Neural Networks for Complete Beginners

learn the relationship between the neurons which make up the human brain and the artificial neurons. Artificial neural networks embrace the;

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Deep Learning from Scratch Building with Python from First Principles

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many;

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Cellular Neural Networks

Cellular Neural Networks (CNN's) were introduced in 1988 by L O Chua and L Yang as a novel class of information processing systems, which;

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