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[资源] Neural Networks for Chemists: An Introduction

This book gives chemists insight into the much discussed and often not fully understood concept of neural networks. The authors pinpoint the five most widely used neural networks and learning strategies, illustrating them with lucid examples. Numerous applications from diverse fields are used in the second part of the book to help the chemist gain a better understanding of neural networks. This self-study guide leads both students and professionals swiftly from introductory principles to practical application. It enables readers to apply neural networks to their problems, either with a commercial neural network package or with a self-made program.

Table of Contents

1 Defining the Area 3
2 Neuron 9
3 Linking Neurons into Networks 37
4 Hopfield Network 53
5 Adaptive Bidirectional Associative Memory (ABAM) 65
6 Kohonen Network 79
7 Counter-Propagation 99
8 Back-Propagation of Errors 119
9 General Comments on Chemical Applications 151
10 Clustering of Multi-Component Analytical Data for Olive Oils 167
11 The Reactivity of Chemical Bonds 183
12 HPLC Optimization of Wine Analysis 197
13 Quantitative Structure-Activity Relationships 205
14 The Electrophilic Aromatic Substitution Reaction 211
15 Modeling and Optimizing a Recipe for a Paint Coating 221
16 Fault Detection and Process Control 229
17 Secondary Structure of Proteins 253
18 Infrared Spectrum-Structure Correlation 261
19 Nonlinear Projection of Molecular Electrostatic Potentials 277
20 Prospects of Neural Networks for Chemical Applications 293

link:
http://rapidshare.com/files/12412846/neunets4chem.rar
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