Neural Networks

Neural Networks - Neural Networks G BHARATH KUMAR B.TECH...

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Neural Networks G BHARATH KUMAR, B.TECH ¾ C.S.E, UNIVERSITY COLLEGE OF ENGINEERING, KAKATIYA UNIVERSITY, KOTHAGUDEM EMAIL-ID:[email protected] PHONE:9246371294 PUNNA MAHESH B.TECH ¾ C.S.E, UNIVERSITY COLLEGE OF ENGINEERING, KAKATIYA UNIVERSITY, KOTHAGUDEM EMAIL-ID:[email protected] PHONE:9959527978A
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Abstract Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced. Historically, the brain has been viewed as a type of computer, and vice-versa, this is true only in the loosest sense. Computers do not provide us with accurate hardware for describing the brain, as they do not possess the parallel processing architectures that have been described in the brain.The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore, there is no need to devise an algorithm in order to perform a specific task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture. They are useful in applications where the complexity of the data or task makes the design of a function by hand impractical A neural network, also known as a parallel distributed processing network, is a computing solution that is loosely modeled after cortical structures of the brain. It consists of interconnected processing elements called nodes or neurons that work together to produce an output function. The output of a neural network relies on the cooperation of the individual neurons within the network to operate. Processing of information by neural networks is characteristically done in parallel rather than in series as in earlier binary computers or Von Neumann machnies
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I. Historical background 1. First Attempt: There were some initial simulations using formal logic. McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked. Their networks were based on simple neurons which were considered to be binary devices with fixed thresholds. The results of their model were simple logic functions such as "a or b" and "a and b". Another attempt was by using computer simulations. Two groups (Farley and Clark, 1954; Rochester, Holland, Haibit and Duda, 1956). The first group (IBM researchers) maintained closed contact with neuroscientists at McGill University. So whenever their models did not work, they consulted the neuroscientists. This interaction established a
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Neural Networks - Neural Networks G BHARATH KUMAR B.TECH...

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