Neural networks can learn unsupervised learning mode

C++ Neural Networks and Fuzzy Logic:Preface

problems with parallel computational architectures. The Hopfield network and feedforward network are introduced in this chapter.

• Chapter 6 focuses on learning and training paradigms. It introduces the concepts of supervised and unsupervised learning, self-organization and topics including backpropagation of errors, radial basis function networks, and conjugate gradient methods.

• Chapter 7 goes through the construction of a backpropagation simulator. You will find this simulator useful in later chapters also. C++ classes and features are detailed in this chapter.

• Chapter 14 applies backpropagation to the problem of financial forecasting, discusses setting up a backpropagation network with 15 input variables and 200 test cases to run a simulation. The problem is approached via a systematic 12-step approach for preprocessing data and setting up the problem. You will find a number of examples of financial forecasting highlighted from the literature. A resource guide for neural networks in finance is included for people who would like more information about this area.

• Chapter 15 deals with nonlinear optimization with a thorough discussion of the Traveling Salesperson problem. You learn the formulation by Hopfield and the approach of Kohonen.

• Chapter 17 discusses some of the latest applications using neural networks and fuzzy logic.

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.

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 rao@cse.bridgeport.edu