C++ for Neural-Networks-and fuzzy logic

在实际开发项目中，使用MATLAB的神经网络工具箱和模糊逻辑工具箱设计算法然后用Coder将算法转换为高级语言。然后使用过Coder的用户就知道，一定有部分应用场景需要直接用高级语言编写算法。这本书就是为此目的编写。 书是以网页形式组织的，其中ewtoc.htm是目录。主要章节如下。 ------------------------------------------------------------- • Chapter 1 gives you an overview of neural network terminology and nomenclature. You discover that 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 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. • 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 to experiment with a program that illustrates the working of this theory. • Chapters 11 and 12 discuss the Self-Organizing map of Teuvo Kohonen and its application to pattern recognition. • Chapter 13 continues the discussion of the backpropagation simulator, with enhancements made to the simulator to include momentum and noise during training. • 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 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.