Neural Computing
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Guide To Neural Computing Applications
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Author : Lionel Tarassenko
language : en
Publisher: Elsevier
Release Date : 1998-01-30
Guide To Neural Computing Applications written by Lionel Tarassenko and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-01-30 with Computers categories.
Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their application must be chosen carefully and appropriately. This book outlines how best to make use of neural networks. It enables newcomers to the technology to construct robust and meaningful non-linear models and classifiers and benefits the more experienced practitioner who, through over familiarity, might otherwise be inclined to jump to unwarranted conclusions. The book is an invaluable resource not only for those in industry who are interested in neural computing solutions, but also for final year undergraduates or graduate students who are working on neural computing projects. It provides advice which will help make the best use of the growing number of commercial and public domain neural network software products, freeing the specialist from dependence upon external consultants.
Handbook Of Neural Computing Applications
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Author : Alianna J. Maren
language : en
Publisher: Academic Press
Release Date : 2014-05-10
Handbook Of Neural Computing Applications written by Alianna J. Maren and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Computers categories.
Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.
Neural Computing An Introduction
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Author : R Beale
language : en
Publisher: CRC Press
Release Date : 1990-01-01
Neural Computing An Introduction written by R Beale and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-01-01 with Mathematics categories.
Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function. A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists.
Neural Computing
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Author : Russell Beale
language : en
Publisher:
Release Date : 1990
Neural Computing written by Russell Beale and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Artificial intelligence categories.
Neural Networks And Statistical Learning
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Author : Ke-Lin Du
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-09
Neural Networks And Statistical Learning written by Ke-Lin Du and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-09 with Technology & Engineering categories.
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Neural Computing Architectures
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Author : Igor Aleksander
language : en
Publisher:
Release Date : 1989
Neural Computing Architectures written by Igor Aleksander and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Artificial intelligence categories.
The Handbook Of Brain Theory And Neural Networks
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Author : Michael A. Arbib
language : en
Publisher: MIT Press
Release Date : 2003
The Handbook Of Brain Theory And Neural Networks written by Michael A. Arbib and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Handbook Of Neural Computation
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Author : E Fiesler
language : en
Publisher: CRC Press
Release Date : 2020-01-15
Handbook Of Neural Computation written by E Fiesler and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-15 with Computers categories.
The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl
Analog Vlsi Neural Networks
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Author : Yoshiyasu Takefuji
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Analog Vlsi Neural Networks written by Yoshiyasu Takefuji and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.
Fuzzy Spiking Neural Networks
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Author : Haider Raza
language : en
Publisher: GRIN Verlag
Release Date : 2012-01-09
Fuzzy Spiking Neural Networks written by Haider Raza and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-09 with Computers categories.
Master's Thesis from the year 2011 in the subject Engineering - Computer Engineering, grade: 8.84, Manav Rachna International University, course: Master of Technology (M.Tech), language: English, abstract: This dissertation presents an introductory knowledge to computational neuroscience and major emphasize on the branch of computational neuroscience called Spiking Neural Networks (SNNs). SNNs are also called the third generation neural networks. It has become now a major field of Soft Computing. In this we talk about the temporal characteristics’ of neuron and studied the dynamics of it. We have presented SNNs architecture with fuzzy reasoning capability. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies and behave in a similar manner as fuzzy membership functions. The network of SNNs consists of three layers that is input, hidden and output layer. The topology of this network is based on Radial basis Network, which can be regarded as universal approximators. The input layer receives the input in the form of frequency which produces the spikes through linear encoding. There is another method of encoding called Poisson encoding; this encoding is used where the data is large. The hidden layer use Receptive Field (RF) to process the input and thus it is frequency selective. The output layer is only responsible for learning. The learning is based on local learning. The XOR classification problem is used to test the capabilities of the network. There is a problem of continuous updating of weight arises. This issue of weight is resolved by using STDP window and fuzzy reasoning. The dissertation demonstrates how it is possible to obtain fuzzy reasoning capability from biological models of spiking neurons. The fuzzy spiking neural network implements fuzzy rules by configuration of receptive fields, antecedent conjunction with excitatory and inhibitory connections, and inferencing via a biologically plausible supervised learning algorithm. In this way, the resulting system utilizes a higher level of knowledge representation.