Tuesday, May 20, 2008


The power and usefulness of artificial neural networks have been demonstrated in several applications including speech synthesis, diagnostic problems, medicine, business and finance, robotic control, signal processing, computer vision and many other problems that fall under the category of pattern recognition. For some application areas, neural models show promise in achieving human-like performance over more traditional artificial intelligence techniques.
What, then, are neural networks? And what can they be used for? Although von-Neumann-architecture computers are much faster than humansin numerical computation, humans are still far better at carrying out low-level tasks such as speech and image recognition. This is due in part to the massive parallelism employed by the brain, which makes it easier to solve problems with simultaneous constraints. It is with this type of problem that traditional artificial intelligence techniques have had limited success. The field of neural networks, however, looks at a variety of models with a structure roughly analogous to that of the set of neurons in the human brain.
The branch of artificial intelligence called neural networks dates back to the 1940s, when McCulloch and Pitts [1943] developed the first neural model. This was followed in 1962 by the perceptron model, devised by Rosenblatt, which generated much interest because of its ability to solve some simple pattern classification problems. This interest started to fade in 1969 when Minsky and Papert [1969] provided mathematical proofs of the limitations of the perceptron and pointed out its weakness in computation. In particular, it is incapable of solving the classic exclusive-or (XOR) problem, which will be discussed later. Such drawbacks led to the temporary decline of the field of neural networks.
The last decade, however, has seen renewed interest in neural netivorks, both among researchers and in areas of application. The development of more-powerful networks, better training algorithms, and improved hardware have all contributed to the revival of the field. Neural-network paradigms in recent years include the Boltzmann machine, Hopfield's network, Kohonen's network, Rumelhart's competitive learning model, Fukushima's model, and Carpenter and Grossberg's Adaptive Resonance Theory model [Wasserman 1989; Freeman and Skapura 1991]. The field has generated interest from researchers in such diverse areas as engineering, computer science, psychology, neuroscience, physics, and mathematics. We describe several of the more important neural models, followed by a discussion of some of the available hardware and software used to implement these models, and a sampling of applications.

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