Practical neural network recipes in C++ / Timothy Masters.
Material type:
TextPublication details: Boston : Academic Press, c1993.Description: xviii, 493 p. : ill. ; 24 cmISBN: - 0124790402
- 23 M423 006.32
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 006.32 M423 (Browse shelf(Opens below)) | Available | C26262 |
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| 006.32 M111 Applications of pulse-coupled neural networks | 006.32 M272 Recurrent neural networks for prediction | 006.32 M324 Hand book of neural computing applications | 006.32 M423 Practical neural network recipes in C++ / | 006.32 M498 Artificial neural networks | 006.32 N342 Bayesian learning for neural networks | 006.32 N342 Bayesian learning for neural networks |
Includes bibliographical references and index.
1. Foundations--
2. Classification--
3. Autoassociation--
4. Time-Series prediction--
5. Function approximation--
6. Multilayer feedforward networks--
7. Eluding local minima I: simulated annealing--
8. Eluding local minima II: genetic optimization--
9. Regression and neural networks--
10. Designing feedforward network architectures--
11. Interpreting weights: how does this thing work--
12. Probabilistic neural networks--
13. Functional link networks--
14. Hybrid networks--
15. Designing the training set--
16. Preparing input data--
17. Fuzzy data and processing--
18. Unsupervised training--
19. Evaluating performance of neural networks--
20. Confidence measures--
21. Optimizing the decision threshold--
22. Using the neural program--
Appendix--
Bibliography--
Index.
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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