An Introduction to Neural Networks



Starch Press, Inc. |-| ISBN 1-59327-076-3 |-| PDF |-| English |-| 283 Pages |-| 3.57 MB |-| No Pass

Introduction
A First wave of interest in neural networks (also known as 'connectionist models' or 'parallel distributed processing') emerged after the introduction of simplied neurons by McCulloch and Pitts in 1943 (McCulloch & Pitts, 1943). These neurons were presented as models of biological neurons and as conceptual components for circuits that could perform computational tasks. When Minsky and Papert published their book Perceptrons in 1969 (Minsky & Papert, 1969) in which they showed the deciencies of perceptron models, most neural network funding was redirected and researchers left the eld. Only a few researchers continued their eorts, most notably Teuvo Kohonen, Stephen Grossberg, James Anderson, and Kunihiko Fukushima. The interest in neural networks re-emerged only after some important theoretical results were attained in the early eighties (most notably the discovery of error back-propagation), and new hardware developments increased the processing capacities. This renewed interest is re
ected in the number
of scientists, the amounts of funding, the number of large conferences, and the number of journals associated with neural networks. Nowadays most universities have a neural networks group, within their psychology, physics, computer science, or biology departments. Articial neural networks can be most adequately characterised as 'computational models' with particular properties such as the ability to adapt or learn, to generalise, or to cluster or organise data, and which operation is based on parallel processing. However, many of the abovementioned properties can be attributed to existing (non-neural) models; the intriguing question is to which extent the neural approach proves to be better suited for certain applications than existing models. To date an equivocal answer to this question is not found. Often parallels with biological systems are described. However, there is still so little known (even at the lowest cell level) about biological systems, that the models we are using for our articial neural
systems seem to introduce an oversimplication of the 'biological' models. In this course we give an introduction to articial neural networks. The point of view we take is that of a computer scientist. We are not concerned with the psychological implication of the networks, and we will at most occasionally refer to biological neural models. We consider neural networks as an alternative computational scheme rather than anything else. These lecture notes start with a chapter in which a number of fundamental properties are discussed. In chapter 3 a number of 'classical' approaches are described, as well as the discussion on their limitations which took place in the early sixties. Chapter 4 continues with the description of attempts to overcome these limitations and introduces the back-propagation learning algorithm. Chapter 5 discusses recurrent networks; in these networks, the restraint that there are no cycles in the network graph is removed. Self-organising networks, which require no external teacher, are
discussed in chapter 6. Then, in chapter 7 reinforcement learning is introduced. Chapters 8 and 9 focus on applications of neural networks in the elds of robotics and image processing respectively. The nal chapters discuss implementational aspects.

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