TY - BOOK ID - 5453351 TI - Neural networks : computational models and applications AU - Tang, Huajin AU - Tan, K. C. AU - Zhang, Yi PY - 2007 VL - v. 53 SN - 1860949X SN - 9783540692256 3540692258 9786610804917 1280804912 3540692266 PB - Berlin, Germany ; New York, New York : Springer, DB - UniCat KW - Neural networks (Computer science) KW - Réseaux neuronaux (Informatique) KW - Natural computation. KW - Electronic books. -- local. KW - Neural networks (Computer science). KW - Civil Engineering KW - Computer Science KW - Applied Mathematics KW - Engineering & Applied Sciences KW - Civil & Environmental Engineering KW - Biologically-inspired computing. KW - Biologically-inspired computing KW - Bio-inspired computing KW - Natural computing KW - Artificial neural networks KW - Nets, Neural (Computer science) KW - Networks, Neural (Computer science) KW - Neural nets (Computer science) KW - Computer science. KW - Artificial intelligence. KW - Applied mathematics. KW - Engineering mathematics. KW - Computer Science. KW - Artificial Intelligence (incl. Robotics). KW - Appl.Mathematics/Computational Methods of Engineering. KW - Engineering KW - Engineering analysis KW - Mathematical analysis KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Informatics KW - Science KW - Mathematics KW - Artificial intelligence KW - Natural computation KW - Soft computing KW - Artificial Intelligence. KW - Mathematical and Computational Engineering. UR - https://www.unicat.be/uniCat?func=search&query=sysid:5453351 AB - Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain. Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily. Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems. ER -