Practical Neural Network Recipies in C++
- 1 Edición - 11 de mayo de 1993
- Última edición
- Autor: Masters
- Idioma: Inglés
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… Leer más
Descripción
Descripción
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.
De interès para
De interès para
researchers, practitioners, developers, programmers, and students using neural networks in computer science or any of the many areas whre neural networks may be applied, including engineering, phusics, and business.
Índice
Índice
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.
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 11 de mayo de 1993
- Idioma: Inglés
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