Artificial neural networks B. Yegnanarayana
Material type: TextLanguage: English Publication details: New Delhi : PHI Learning, 2015.Description: xiii, 461 p.: 22 cmISBN:- 9788120312531
- 006.32 YEN
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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General Books | CUTN Central Library Generalia | Non-fiction | 006.32 YEN (Browse shelf(Opens below)) | Available | 33908 | |
General Books | CUTN Central Library Generalia | Non-fiction | 006.32 YEN (Browse shelf(Opens below)) | Available | 33909 | |
General Books | CUTN Central Library Generalia | Non-fiction | 006.32 YEN (Browse shelf(Opens below)) | Available | 33910 | |
General Books | CUTN Central Library Generalia | Non-fiction | 006.32 YEN (Browse shelf(Opens below)) | Available | 33911 | |
Reference Books | CUTN Central Library Generalia | Reference | 006.32 YEN (Browse shelf(Opens below)) | Not For Loan | 33912 |
Browsing CUTN Central Library shelves, Shelving location: Generalia, Collection: Reference Close shelf browser (Hides shelf browser)
006.3 JAN Neuro-fuzzy and soft computing : | 006.3 MIS Artificial intelligence | 006.3 PAT Introduction to Artificial Intelligence and Expert Systems | 006.32 YEN Artificial neural networks | 006.35 AKS Natural language processing : | 006.5 RAJ Digital Logic and Computer Optimization | 006.6 ALA Computer graphics |
Basics of Artificial Neural Networks Activation and Synaptic Dynamics Functional Units of ANN for Pattern Recognition Tasks Feedforward Neural Networks Feedback Neural Networks Competitive Learning Neural Networks Architectures for Complex Pattern Recognition Tasks Applications of ANN
Designed as an introductory level textbook on Artificial Neural Networks at the postgraduate and senior undergraduate levels in any branch of engineering, this self-contained and well-organized book highlights the need for new models of computing based on the fundamental principles of neural networks.
Professor Yegnanarayana compresses, into the covers of a single volume, his several years of rich experience, in teaching and research in the areas of speech processing, image processing, artificial intelligence and neural networks. He gives a masterly analysis of such topics as Basics of artificial neural networks, Functional units of artificial neural networks for pattern recognition tasks, Feedforward and Feedback neural networks, and Archi-tectures for complex pattern recognition tasks. Throughout, the emphasis is on the pattern processing feature of the neural networks. Besides, the presentation of real-world applications provides a practical thrust to the discussion.
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