CHARACTERISTICS OF ARTIFICIAL NEURAL NETWORKS
Artificial neural networks are biologically inspired and are composed of elements that perform in a manner that is analogous to the most elementary function of the biological neuron. These elements are then organized in away that may be related to the anatomy of the brain.Despite the superficial resemblance, artificial neural networks exhibits a surprising number of brain’s characteristics. i.e Thay learn from experience, generalized from previous examples to the new ones and abstract essential characteristics from input containing irrelevant data. But the learning of the artificial networks are limited and many difficult problem remains to be unsolved. And the ANN are not applicable for pay role calculations. They better perform in pattern recognition .It is expected that ANN will soon duplicate the function of the brain.
Artificial neural networks can modify their behavior in response to their environment. This factor, more than any other, is responsible for the interest they have received. Shown a set of inputs (perhaps with desired outputs), they self-adjust to produce consistent response. A wide variety of training algorithms has been developed, each with its own strengths and weaknesses. As we point out later in this volume, there are important questions yet to be answered regarding what things a network can be trained to do, and how the training should be performed, Artificial neural networks exhibit a surprising number ofthe brain’s characteristics. For example, they learn from experience, neralize from previousexamples to new ones, and abstract essential characteristics from inputs
containing irrelevant data.
Despite these functional similarities, not even the most optimisticadvocate will suggest that artificial neural networks will soon duplicate the functions of the human brain. The actual “intelligence”exhibited by the most sophisticated artificial neural networksis below the level of a tapeworm; enthusiasm must be tempered by current reality. It is, however, equallv incorrect to ignore the surprisingly brain like performance of certain artificial neural networks. These abilities, however limited they are today, hint that a deep understanding of human intelligence may lie close at hand, and along with it a host of revolutionary applications.
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