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Practical Neural Network Recipes in C++ by Timothy Masters (1993, Trade Paperback)

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This text serves as a cookbook for neural network solutions to practical problems using C++. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance.

About this product

Product Identifiers

PublisherElsevier Science & Technology Books

ISBN-100124790402

ISBN-139780124790407

eBay Product ID (ePID)61114

Product Key Features

Number of Pages493 Pages

Publication NamePractical Neural Network Recipes in C++

LanguageEnglish

SubjectProgramming Languages / C++, Intelligence (Ai) & Semantics, Neural Networks

Publication Year1993

TypeTextbook

Subject AreaComputers

AuthorTimothy Masters

FormatTrade Paperback

Dimensions

Item Height1 in

Item Weight32.2 Oz

Item Length9.2 in

Item Width7.5 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN92-047469

Dewey Edition20

IllustratedYes

Dewey Decimal005.133

Table Of ContentFoundations. Classification. Autoassociation. Time Series Prediction. Function Approximation. Multilayer Feedforward Networks. Eluding Local Minimai: Simulated Annealing. Eluding Local Minima II: Genetic Optimisation. Regression and Neural Networks. Designing Feedforward Network Architectures. Interpreting Weights: How Does This Thing Work? Probalistic Neural Networks. Functional Link Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Confidence Measures. Optimizing the Decision Threshold. Using the NEURAL Program. Appendix. Bibliography. Index.

SynopsisThis text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assumed and all models are presented from the ground up.The principle focus of the book is the three layer feedforward network, for more than a decade as the workhorse of professional arsenals. Other network models with strong performance records are also included.Bound in the book is an IBM diskette that includes the source code for all programs in the book. Much of this code can be easily adapted to C compilers. In addition, the operation of all programs is thoroughly discussed both in the text and in the comments within the code to facilitate translation to other languages.