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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 |
Index | 295 |
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Add Neural Networks for Chemists: An Introduction, '... highly recommended ... could become a scientific bestseller ...' Spectroscopy Europe This self-study guide leads both students and professionals surely and swiftly from introductory principles to practical applications of neural networks. It, Neural Networks for Chemists: An Introduction to the inventory that you are selling on WonderClubX
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Add Neural Networks for Chemists: An Introduction, '... highly recommended ... could become a scientific bestseller ...' Spectroscopy Europe This self-study guide leads both students and professionals surely and swiftly from introductory principles to practical applications of neural networks. It, Neural Networks for Chemists: An Introduction to your collection on WonderClub |