Elements of Artificial Neural Networks

Name: Elements of Artificial Neural Networks
Price: 67.88 USD
Availability: InStock
ISBN: 9780262133289

ISBN-10: 0262133288

ISBN-13: 9780262133289

Edition: 1997

Authors: Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka

List price: $75.00

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Description:

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses…

Book details

List price: $75.00
Copyright year: 1997
Publisher: MIT Press
Publication date: 10/11/1996
Binding: Hardcover
Pages: 400
Size: 7.25" wide x 9.50" long x 1.00" tall
Weight: 2.046
Language: English



Preface



Introduction



History of Neural Networks



Structure and Function of a Single Neuron



Biological neurons



Artificial neuron models



Neural Net Architectures



Fully connected networks



Layered networks



Acyclic networks



Feedforward networks



Modular neural networks



Neural Learning



Correlation learning



Competitive learning



Feedback-based weight adaptation



What Can Neural Networks Be Used for?



Classification



Clustering



Vector quantization



Pattern association



Function approximation



Forecasting



Control applications



Optimization



Search



Evaluation of Networks



Quality of results



Generalizability



Computational resources



Implementation



Conclusion



Exercises



Supervised Learning: Single-Layer Networks



Perceptrons



Linear Separability



Perceptron Training Algorithm



Termination criterion



Choice of learning rate



Non-numeric inputs



Guarantee of Success



Modifications



Pocket algorithm



Adalines



Multiclass discrimination



Conclusion



Exercises



Supervised Learning: Multilayer Networks I



Multilevel Discrimination



Preliminaries



Architecture



Objectives



Backpropagation Algorithm



Setting the Parameter Values



Initialization of weights



Frequency of weight updates



Choice of learning rate



Momentum



Generalizability



Number of hidden layers and nodes



Number of samples



Theoretical Results*



Cover's theorem



Representations of functions



Approximations of functions



Accelerating the Learning Process



Quickprop algorithm



Conjugate gradient



Applications



Weaning from mechanically assisted ventilation



Classification of myoelectric signals



Forecasting commodity prices



Controlling a gantry crane



Conclusion



Exercises



Supervised Learning: Mayer Networks II



Madalines



Adaptive Multilayer Networks



Network pruning algorithms



Marchand's algorithm



Upstart algorithm



Neural Tree



Cascade correlation



Tiling algorithm



Prediction Networks



Recurrent networks



Feedforward networks for forecasting



Radial Basis Functions



Polynomial Networks



Regularization



Conclusion



Exercises



Unsupervised Learning



Winner-Take-All Networks



Hamming networks



Maxnet



Simple competitive learning



Learning Vector Quantizers



Counterpropagation Networks



Adaptive Resonance Theory



Topologically Organized Networks



Self-organizing maps



Convergence*



Extensions



Distance-Based Learning



Maximum entropy



Neural gas



Neocognitron



Principal Component Analysis Networks



Conclusion



Exercises



Associative Models



Non-iterative Procedures for Association



Hopfield Networks



Discrete Hopfield networks



Storage capacity of Hopfield networks*



Continuous Hopfield networks



Brain-State-in-a-Box Network



Boltzmann Machines



Mean field annealing



Hetero-associators



Conclusion



Exercises



Optimization Methods



Optimization using Hopfield Networks



Traveling salesperson problem



Solving simultaneous linear equations



Allocating documents to multiprocessors


Discrete Hopfield network


Continuous Hopfield network


Performance



Iterated Gradient Descent



Simulated Annealing



Random Search



Evolutionary Computation



Evolutionary algorithms



Initialization



Termination criterion



Reproduction



Operators


Mutation


Crossover



Replacement



Schema Theorem*



Conclusion



Exercises



A Little Math



Calculus



Linear Algebra



Statistics



Data



Iris Data



Classification of Myoelectric Signals



Gold Prices



Clustering Animal Features



3-D Corners, Grid and Approximation



Eleven-City Traveling Salesperson Problem (Distances)



Daily Stock Prices of Three Companies, over the Same Period



Spiral Data


Bibliography


Index