Artificial Neural Network and Supervised Learning
Artificial neural networks are biologically inspired but not necessarily biologically plausible. Researchers are usually thinking about the organization of the brain when considering network configurations and algorithms. But the knowledge about the brain’s overall operation is so limited that there is little to guide those who would emulate it. Hence, at present time biologists, psychologists, computer scientists, physicists and mathematicians are working all over the world to learn more and more about the brain. Interests in neural network differ according to profession like neurobiologists and psychologists try to understanding brain. Engineers and physicists use it as tool to recognize patterns in noisy data, business analysts and engineers use to model data, computer scientists and mathematicians viewed as a computing machines that may be taught rather than programmed and artificial intelligentsia, cognitive scientists and philosophers use as sub-symbolic processing (reasoning with patterns, not symbols), etc.
A conventional computer will never operate as brain does, but it can be used to simulate or model human thought. In 1955, Herbert Simon and Allen Newell announced that they had invented a thinking machine. Their program, the logic theorist, dealt with problems of proving theories based on assumptions it was given. Simon and Newell later developed the general problem solver, which served as the basis of artificial intelligence (AI) systems. Simon and Newell believed that the main task of AI was figuring out the nature of the symbols and rules that the mind uses. For many years AI engineers have used the "top-down" approach to create intelligent machinery. The top-down approach starts with the highest level of complexity, in this case thought, and breaks it down into smaller pieces to work with. A procedure is followed step by step. AI engineers write very complex computer programs to solve problems. Another approach to the modelling of brain functioning starts with the lowest level, the single neuron. This could be referred to as a bottom-up approach to modelling intelligence.
KeywordsNeural Network Hide Layer Supervise Learn Less Mean Square Aggregation Function
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