• Title Recurrent Neural Networks
• Author(s) Xiaolin Hu and P. Balasubramaniam
• Publisher: IN-TECH (September 2008); eBook (Creative Commons Licensed)
• License(s): Attribution 3.0 Unported (CC BY 3.0)
• Paperback 400 pages
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-953-7619-08-4

Book Description

This book investigates the following recurrent neural networks (RNNs) models which solve some practical problems, together with their corresponding analysis on stability and convergence. A type of multilayer pole-assignment neural networks is applied to online synthesizing and tuning feedback control systems.

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. The second part of the book consists of seven chapters, all of which are about system identification and control. The third part of the book is composed of Chapter 11 and Chapter 12, where two interesting RNNs are discussed, respectively.The fourth part of the book comprises four chapters focusing on optimization problems. Doing optimization in a way like the central nerve systems of advanced animals including humans is promising from some viewpoints.

• N/A

• Amazon

• Neural Networks and Deep Learning
• Machine Learning
• Artificial Intelligence

• Recurrent Neural Networks (Xiaolin Hu, et al.)
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