'This book is distinctive in that it implements nodes and links as base objects and then composes them into four different kinds of neural networks. Roger's writing is clear...The text and code are both quite readable. Overall, this book will be useful to anyone who wants to implement neural networks in C++ (and, to a lesser extent, in other object-oriented programming languages.)...I recommend this book to anyone who wants to implement neural networks in C++' - D.L. Chester, Newark, Delaware in Computing reviews. "Object-Oriented Neural Networks in C++" is a valuable tool for anyone who wants to understand, implement, or utilize neural networks. This book/disk package provides the reader with a foundation from which any neural network architecture can be constructed. The author has employed object-oriented design and object-oriented programming concepts to develop a set of foundation neural network classes, and shows how these classes can be used to implement a variety of neural network architectures with a great deal of ease and flexibility. A wealth of neural network formulas (with standardized notation), object code implementations, and examples are provided to demonstrate the object-oriented approach to neural network architectures and to facilitate the development of new neural network architectures. This is the first book to take full advantage of the reusable nature of neural network classes. This book describes how to use the classes provided to implement a variety of neural network architectures including ADALINE, Back propagation, Self-Organizing, and BAM. It provides a set of reusable neural network classes, created in C++, capable of implementing any neural network architecture. It includes an IBM disk of the source code for the classes, which is platform independent. It includes an IBM disk with C++ programs described in the book.