C++ Neural Networks and Fuzzy Logic: Preface


•  Chapter 2 introduces C++ and object orientation. You learn the benefits of object−oriented programming and its basic concepts. •



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  Chapter 2 introduces C++ and object orientation. You learn the benefits of object−oriented

programming and its basic concepts.



  Chapter 3 introduces fuzzy logic, a technology that is fairly synergistic with neural network

problem solving. You learn about math with fuzzy sets as well as how you can build a simple

fuzzifier in C++.

  Chapter 4 introduces you to two of the simplest, yet very representative, models of: the Hopfield

network, the Perceptron network, and their C++ implementations.



  Chapter 5 is a survey of neural network models. This chapter describes the features of several

models, describes threshold functions, and develops concepts in neural networks.



  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 8 covers the Bidirectional Associative memories for associating pairs of patterns.

C++ Neural Networks and Fuzzy Logic:Preface

Preface

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