Practical Neural Network Recipes in C++

Practical Neural Network Recipes in C++

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Description

This 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.
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Product details

  • Mixed media product | 493 pages
  • 186 x 230 x 28mm | 898.11g
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • bibliography, index
  • 0124790402
  • 9780124790407
  • 1,390,368

Table of contents

Foundations. 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.
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18 ratings
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