Hebbian Learning and Negative Feedback Networks (Advanced Information and Knowledge Processing)
Edition: 1 edition
Publisher: Springer
Published: December 1, 2004
Language: eng
Format: Hardcover
Number of pages: 383
ISBN-13: 9781852338831
Subjects: Artificial intelligence; General; Computer Vision; Computers; Computers - General Information; Computer Books: General; Neural networks (Computer scie; Artificial Intelligence - General; Computer Science; Artificial neural networks; Computers / Computer Science; Data mining; Exploratory data analyis; Hebbian learning; Machine learning; Signal processing; Unsupervised learning; Neural networks (Computer science)
First sentence: We report, in this book, on the last decade's research into a single architecture of artificial neural networks.
Description: This book is the outcome of a decade’s research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was “Negative Feedback as an Organising Principle for Arti?cial Neural Networks”. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from • Dr. Darryl Charles [24] in Chapter 5. • Dr. Stephen McGlinchey [127] in Chapter 7. • Dr. Donald MacDonald [121] in Chapters 6 and 8. • Dr. Emilio Corchado [29] in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti?cial neural networks.