| C++ Neural Networks and Fuzzy Logic |
by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN: 1558515526 Pub Date: 06/01/95
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The number of models available in neural network literature is quite large. Very often the treatment is mathematical and complex. This book provides illustrative examples in C++ that the reader can use as a basis for further experimentation. A key to learning about neural networks to appreciate their inner workings is to experiment. Neural networks, in the end, are fun to learn about and discover. Although the language for description used is C++, you will not find extensive class libraries in this book. With the exception of the backpropagation simulator, you will find fairly simple example programs for many different neural network architectures and paradigms. Since backpropagation is widely used and also easy to tame, a simulator is provided with the capacity to handle large input data sets. You use the simulator in one of the chapters in this book to solve a financial forecasting problem. You will find ample room to expand and experiment with the code presented in this book.
There are many different angles to neural networks and fuzzy logic. The fields are expanding rapidly with ever-new results and applications. This book presents many of the different neural network topologies, including the BAM, the Perceptron, Hopfield memory, ART1, Kohonens Self-Organizing map, Koskos Fuzzy Associative memory, and, of course, the Feedforward Backpropagation network (aka Multilayer Perceptron). You should get a fairly broad picture of neural networks and fuzzy logic with this book. At the same time, you will have real code that shows you example usage of the models, to solidify your understanding. This is especially useful for the more complicated neural network architectures like the Adaptive Resonance Theory of Stephen Grossberg (ART).
The subjects are covered as follows:
In this second edition, we have followed readers suggestions and included more explanations and material, as well as updated the material with the latest information and research. We have also corrected errors and omissions from the first edition.
Neural networks are now a subject of interest to professionals in many fields, and also a tool for many areas of problem solving. The applications are widespread in recent years, and the fruits of these applications are being reaped by many from diverse fields. This methodology has become an alternative to modeling of some physical and nonphysical systems with scientific or mathematical basis, and also to expert systems methodology. One of the reasons for it is that absence of full information is not as big a problem in neural networks as it is in the other methodologies mentioned earlier. The results are sometimes astounding, even phenomenal, with neural networks, and the effort is at times relatively modest to achieve such results. Image processing, vision, financial market analysis, and optimization are among the many areas of application of neural networks. To think that the modeling of neural networks is one of modeling a system that attempts to mimic human learning is somewhat exciting. Neural networks can learn in an unsupervised learning mode. Just as human brains can be trained to master some situations, neural networks can be trained to recognize patterns and to do optimization and other tasks.
In the early days of interest in neural networks, the researchers were mainly biologists and psychologists. Serious research now is done by not only biologists and psychologists, but by professionals from computer science, electrical engineering, computer engineering, mathematics, and physics as well. The latter have either joined forces, or are doing independent research parallel with the former, who opened up a new and promising field for everyone.
In this book, we aim to introduce the subject of neural networks as directly and simply as possible for an easy understanding of the methodology. Most of the important neural network architectures are covered, and we earnestly hope that our efforts have succeeded in presenting this subject matter in a clear and useful fashion.
We welcome your comments and suggestions for this book, from errors and oversights, to suggestions for improvements to future printings at the following E-mail addresses:
V. Rao email@example.com
H. Rao ViaSW@aol.com
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