Singh - C++ Neural Networks And Fuzzy Logic

C++ Neural Networks and Fuzzy Logicby virender singh
Table of Contents

The number of models available in neural network literature is quite large. Very often the treatment ismathematical and complex. This book provides illustrative examples in C++ that the reader can use as a basisfor further experimentation. A key to learning about neural networks to appreciate their inner workings is toexperiment. Neural networks, in the end, are fun to learn about and discover. Although the language fordescription used is C++, you will not find extensive class libraries in this book. With the exception of thebackpropagation simulator, you will find fairly simple example programs for many different neural networkarchitectures and paradigms. Since backpropagation is widely used and also easy to tame, a simulator isprovided with the capacity to handle large input data sets. You use the simulator in one of the chapters in thisbook to solve a financial forecasting problem. You will find ample room to expand and experiment with thecode presented in this book.

There are many different angles to neural networks and fuzzy logic. The fields are expanding rapidly withevernew results and applications. This book presents many of the different neural network topologies,including the BAM, the Perceptron, Hopfield memory, ART1, Kohonens SelfOrganizing map, KoskosFuzzy Associative memory, and, of course, the Feedforward Backpropagation network (aka MultilayerPerceptron). You should get a fairly broad picture of neural networks and fuzzy logic with this book. At thesame time, you will have real code that shows you example usage of the models, to solidify yourunderstanding. This is especially useful for the more complicated neural network architectures like theAdaptive Resonance Theory of Stephen Grossberg (ART).

The subjects are covered as follows:

Chapter 1 gives you an overview of neural network terminology and nomenclature. You discoverthat neural nets are capable of solving complex problems with parallel computational architectures.The Hopfield network and feedforward network are introduced in this chapter.
Chapter 2 introduces C++ and object orientation. You learn the benefits of objectorientedprogramming and its basic concepts.
Chapter 3 introduces fuzzy logic, a technology that is fairly synergistic with neural networkproblem solving. You learn about math with fuzzy sets as well as how you can build a simplefuzzifier in C++.
Chapter 4 introduces you to two of the simplest, yet very representative, models of: the Hopfieldnetwork, the Perceptron network, and their C++ implementations.
Chapter 5 is a survey of neural network models. This chapter describes the features of severalmodels, describes threshold functions, and develops concepts in neural networks.
Chapter 6 focuses on learning and training paradigms. It introduces the concepts of supervisedand unsupervised learning, selforganization and topics including backpropagation of errors, radialbasis function networks, and conjugate gradient methods.
Chapter 7 goes through the construction of a backpropagation simulator. You will find thissimulator useful in later chapters also. C++ classes and features are detailed in this chapter.
Chapter 8 covers the Bidirectional Associative memories for associating pairs of patterns.

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Chapter 9 introduces Fuzzy Associative memories for associating pairs of fuzzy sets.
Chapter 10 covers the Adaptive Resonance Theory of Grossberg. You will have a chance toexperiment with a program that illustrates the working of this theory.
Chapters 11 and 12 discuss the SelfOrganizing map of Teuvo Kohonen and its application topattern recognition.
Chapter 13 continues the discussion of the backpropagation simulator, with enhancements madeto the simulator to include momentum and noise during training.
Chapter 14 applies backpropagation to the problem of financial forecasting, discusses setting up abackpropagation network with 15 input variables and 200 test cases to run a simulation. The problemis approached via a systematic 12step approach for preprocessing data and setting up the problem.You will find a number of examples of financial forecasting highlighted from the literature. Aresource guide for neural networks in finance is included for people who would like more informationabout this area.
Chapter 15 deals with nonlinear optimization with a thorough discussion of the TravelingSalesperson problem. You learn the formulation by Hopfield and the approach of Kohonen.
Chapter 16 treats two application areas of fuzzy logic: fuzzy control systems and fuzzy databases.This chapter also expands on fuzzy relations and fuzzy set theory with several examples.
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, aswell as updated the material with the latest information and research. We have also corrected errors andomissions from the first edition.

Neural networks are now a subject of interest to professionals in many fields, and also a tool for many areas ofproblem solving. The applications are widespread in recent years, and the fruits of these applications are beingreaped by many from diverse fields. This methodology has become an alternative to modeling of somephysical and nonphysical systems with scientific or mathematical basis, and also to expert systemsmethodology. One of the reasons for it is that absence of full information is not as big a problem in neuralnetworks as it is in the other methodologies mentioned earlier. The results are sometimes astounding, evenphenomenal, with neural networks, and the effort is at times relatively modest to achieve such results. Imageprocessing, vision, financial market analysis, and optimization are among the many areas of application ofneural networks. To think that the modeling of neural networks is one of modeling a system that attempts tomimic 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 recognizepatterns 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 computerscience, electrical engineering, computer engineering, mathematics, and physics as well. The latter have eitherjoined forces, or are doing independent research parallel with the former, who opened up a new and promisingfield for everyone.

In this book, we aim to introduce the subject of neural networks as directly and simply as possible for an easyunderstanding of the methodology. Most of the important neural network architectures are covered, and weearnestly 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 forimprovements to future printings at the following Email addresses:
Virender Singh
Table of Contents
C++ Neural Networks and Fuzzy Logicby Virender Singh

Neural Processing
Neural Network
Output of a Neuron
Cash Register Game
Weights
Training
Feedback
Supervised or Unsupervised Learning
Noise
Memory
Capsule of History
Neural Network Construction
Sample Applications

Qualifying for a Mortgage
Cooperation and Competition
ExampleA FeedForward Network
ExampleA Hopfield Network

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