Covering the fundamental theory and practical implementation of various neural models, this text provides a coherent exploration and a well structured presentation of the three most important aspects of the neural networks: application, algorithm, and architecture. It offers working knowledge of the various neural models, the fundamental theoretical basis, the potential application domains, and the basic implementation issues. Electrical Engineers, Computer Engineers and Computer Scientists will find this text invaluable.
FREE Two-Day Shipping for students on millions of items. Learn more |
| ||||||||||||||||||
Book Description
Editorial Reviews
From the Back Cover
Covering the fundamental theory and practical implementation of various neural models, this text provides a coherent exploration and a well structured presentation of the three most important aspects of the neural networks: application, algorithm, and architecture. It offers working knowledge of the various neural models, the fundamental theoretical basis, the potential application domains, and the basic implementation issues. Electrical Engineers, Computer Engineers and Computer Scientists will find this text invaluable.
Product Details
Would you like to update product info or give feedback on images?
|
More About the Author
Discover books, learn about writers, read author blogs, and more.
Customer Reviews
|
There are no customer reviews yet.
|
|||
|
Video reviews
|
Inside This Book
(learn more)
Key Phrases - Statistically Improbable Phrases (SIPs):
(learn more)
stochastic neural networks, vector quantizer, arithmetic processing, lateral orthogonalization network, normalized perceptron, processor element design requirements, orthogonalization rule, individual training strategies, neural processing circuits, antireinforced learning, digital neurocomputers, retrieving phase, pass vigilance test, temporal dynamic models, orthogonalization networks, multiple principal components, pipelining period, instantiation space, static pattern recognition, schedule vector, competitive learning networks, vigilance threshold, stochastic simulated, linear perceptron, systolic design Key Phrases - Capitalized Phrases (CAPs): (learn more)
Optimization Neural Networks Chap, Decision-Based Neural Networks Chap, Principal Component Neural Networks Chap, Overview Chap, Stochastic Temporal Networks, Deterministic Temporal Neural Networks Chap, Hidden Markov Models Chap, Mapping Neural Nets, Fixed-Weight Associative Memory Networks Chap, Competitive Learning Networks Chap, General-Purpose Digital Neurocomputers, Connection Machine, Nonlinear Multilayer Back-Propagation Networks, Symmetric Principal Component Analysis, Training Versus Generalization Performances, Princeton Engine, Problems Exercise, Linear Temporal Dynamic Models, Taxonomy of Neural Networks, Dedicated Neural Processing Circuits, Linear Perception Networks, Character Recognition, Retrieving Phase of Hidden Markov Models, Adaptive Clustering Techniques, Concluding Remarks Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
stochastic neural networks, vector quantizer, arithmetic processing, lateral orthogonalization network, normalized perceptron, processor element design requirements, orthogonalization rule, individual training strategies, neural processing circuits, antireinforced learning, digital neurocomputers, retrieving phase, pass vigilance test, temporal dynamic models, orthogonalization networks, multiple principal components, pipelining period, instantiation space, static pattern recognition, schedule vector, competitive learning networks, vigilance threshold, stochastic simulated, linear perceptron, systolic design Key Phrases - Capitalized Phrases (CAPs): (learn more)
Optimization Neural Networks Chap, Decision-Based Neural Networks Chap, Principal Component Neural Networks Chap, Overview Chap, Stochastic Temporal Networks, Deterministic Temporal Neural Networks Chap, Hidden Markov Models Chap, Mapping Neural Nets, Fixed-Weight Associative Memory Networks Chap, Competitive Learning Networks Chap, General-Purpose Digital Neurocomputers, Connection Machine, Nonlinear Multilayer Back-Propagation Networks, Symmetric Principal Component Analysis, Training Versus Generalization Performances, Princeton Engine, Problems Exercise, Linear Temporal Dynamic Models, Taxonomy of Neural Networks, Dedicated Neural Processing Circuits, Linear Perception Networks, Character Recognition, Retrieving Phase of Hidden Markov Models, Adaptive Clustering Techniques, Concluding Remarks Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
46
books
cite this book:
See all 46 books citing this book
- Pattern Recognition with Neural Networks in C++ by Abhijit S. Pandya on 6 pages
- Handbook of Neural Networks for Speech Processing (Artech House Signal Processing Library) by Shigeru Katagiri on page 18, page 119, and page 343
- Handbook of Neural Network Signal Processing (Electrical Engineering & Applied Signal Processing Series) by Yu Hen Hu on page 14, and page 79
- Soft Computing in Human-Related Sciences (International Series on Computational Intelligence) by Horia-Nicolai L Teodorescu on page 89, and page 195
- Neural Networks and Systolic Array Design by Sankar K. Pal on page 45, and page 250
See all 46 books citing this book
Tags Customers Associate with This Product(What's this?)Click on a tag to find related items, discussions, and people.
|
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
Customer Discussions
|
This product's forum
Active discussions in related forums
Search Customer Discussions
|
Related forums
|
Listmania!
So You'd Like to...
Look for Similar Items by Category
- Books > Computers & Technology > Certification
- Books > Computers & Technology > Computer Science > Artificial Intelligence > Neural Networks
- Books > Computers & Technology > Programming > Algorithms
- Books > New & Used Textbooks > Computer Science
- Books > New & Used Textbooks > Engineering > Electrical & Electronic Engineering
- Books > Professional & Technical > Engineering
- Books > Science & Math > Mathematics
