Recurrent Neural Networks
by Xiaolin Hu, P. Balasubramaniam
Publisher: InTech 2008
Number of pages: 400
The concept of neural network originated from neuroscience, and one of its primitive aims is to help us understand the principle of the central nerve system and related behaviors through mathematical modeling. The first part of the book is a collection of three contributions dedicated to this aim.
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by Ben Goertzel - Plenum Press
This text applies the concepts of complexity science to provide an explanation of all aspects of human creativity. The book describes the model that integrates ideas from computer science, mathematics, neurobiology, philosophy, and psychology.
by Milan Hajek - University of KwaZulu-Natal
Contents: Introduction; Learning process; Perceptron; Back-propagation networks; The Hopfield network; Self-organizing feature maps; Temporal processing with neural networks; Radial-basis function networks; Adaline (Adaptive Linear System).
Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks.
by David Kriesel - dkriesel.com
Text and illustrations should be memorable and easy to understand to offer as many people as possible access to the field of neural networks. The chapters are individually accessible to readers with little previous knowledge.