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Neural Networks for Chemists: An Introduction
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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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