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, Kohonen’s Self-Organizing map, Kosko’s 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).