Neural networks Hopfield model

Chapter 10 covers another important field with a great overlap with CA neural networks. Beginning with a short historical survey of what is really an independent field, chapter 10 discusses the Hopfield model, stochastic nets, Boltzman machines, and multi-layered perceptrons. 

An important aspect that did come forward in the 1970s was that of self-organizing maps (SOMs). SOMs will be discussed later in this encyclopedia. In 1982, John Hopfield of Caltech presented a paper to the scientific community in which he stated that the approach to AI should not be to purely imitate the human brain but instead to use its concepts to buUd machines that could solve dynamic problems. He showed what such networks were capable of and how they would work. It was his articulate, likable character and his vast knowledge of mathematical analysis that convinced scientists and researchers at the National Academy of Sciences to renew interest into the research of AI and neural networks. His ideas gave birth to a new class of neural networks that over time became known as the Hopfield model. 

More than 50 different types of neural network exist. Certain networks are more efficient in optimization others perform better in data modeling and so forth. According to Basheer (2000) the most popular neural networks today are the Hopfield networks, the Adaptive Resonance Theory (ART) networks, the Kohonen networks, the counter propagation networks, the Radial Basis Function (RBF) networks, the backpropagation networks and recurrent networks. 

Feedback Dynamics. In assuming the counteractive rule, the optical feedback system automatically updates the illumination pattern. The feedback is implemented with a discrete form of recurrent neural network dynamics known as Hopfield-Tank model [8] yi t + At) = 1 — where Wij is the... 

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