Principles of artificial neural networks / Daniel Graupe.
Material type:
TextSeries: Advanced series on circuits and systems ; v 7.Publication details: Singapore : World Scientific, ©2013.Edition: 3rd edDescription: xviii, 363 pages : illustrations ; 26 cmISBN: - 9789814522731
- 006.32 23 G774
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 006.32 G774 (Browse shelf(Opens below)) | Available | 137588 |
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| 006.32 G181 Neural networks theory | 006.32 G558 Neurocomputers : an overview of neural networks in VLSI | 006.32 G774 Principles of artificial neural networks | 006.32 G774 Principles of artificial neural networks / | 006.32 H155 Synergetic computers and cognition | 006.32 H198 Principles of neurocomputing for science and engineering | 006.32 H224 Learning with recurrent neural networks |
Includes bibliographical references and indexes.
1. Introduction and role of artificial neural networks --
2. Fundamentals of biological neural networks --
3. Basic principles of ANNs and their early structures --
4. The perceptron --
5. The madaline --
6. Back propagation --
7. Hopfield networks --
8. Counter propagation --
9. Large scale memory storage and retrieval (LAMSTAR) network --
10. Adaptive resonance theory --
11. The cognition and the neocognition --
12. Statistical training --
13. Recurrent (time cycling) back propagation networks.
Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition - all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.
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