ARTIFICIAL NEURAL NETWORKS
The developments in Artificial Intelligence (AI) appear promising, but when applied to real world intelligent task such as in speech, vision and natural language processing, the AI techniques show their inadequacies and ‘brittleness’ in the sense that they become highly task specific. The computing models inspired by biological neural networks can provide new directions to solve problems arising in natural tasks.
The purpose of this paper is to discuss the Characteristics and Applications of Artificial Neural Networks. In Characteristics of Neural Networks, we will discuss about the Features of Biological Neural Networks and Performance comparison of computer and Biological Neural Networks. In applications we discuss about Direct applications which include Pattern classification, Associative memories, Optimization and Control applications and Application Areas. At last we have conclusion and Bibliography
CHARACTERISTICS OF NEURAL NETWORKS
FEATURES OF BIOLOGICAL NEURAL NETWORKS:
Some attractive features of the biological neural network that made it superior to even the most sophisticated Artificial Intelligence computer system for pattern recognition tasks are the following: • Robustness and fault tolerance: The decay of nerve cells does not seem to affect the performance significantly. • Flexibility: The network automatically adjusts to a new environment without using any preprogrammed instructions. • Ability to deal with a variety of data situations: The network can deal with information that is fuzzy, probabilistic, noisy and inconsistent. • Collective computation: The network performs routinely many operations in parallel and also a given task in a distributed manner. PERFORMANCE COMPARISION OF COMPUTER AND BIOLOGICAL NEURAL NETWORKS: A set of processing units when assembled in a closely interconnected network, offers a surprisingly rich structure exhibiting some features of the biological neural network. Such a structure is called an artificial neural network (ANN). Since ANNs are implemented on computers, it is worth comparing the processing capabilities of a computer with those of brain.
Speed: - Neural networks are slow in processing information. For the most advanced computers the cycle time corresponding to execution of one step of a program in the central processing unit is in the range of a few nanoseconds. The cycle time corresponding to neural event prompted by an external stimulus occurs in milliseconds range. Thus the computer processes information nearly a million times faster. Processing: - Neural networks can perform massively parallel operations. Most programs have large number of instructions, and they operate in a sequential mode one instruction after another on a conventional computer. On the other hand, the brain operates with massively parallel operations, each of them having comparatively fewer steps. This explains the superior performance of human information processing for certain tasks, despite being several orders of magnitude slower compared to computer processing of information. Size And Complexity: - Neural networks have large number of computing elements , and the corresponding is not restricted to with in neurons. The number of neurons in a brain is estimated to be about 10^11 and the total number interconnections to be around 10^15. It is this size and complexity of connections that may be giving the brain the power of performing complex pattern recognition tasks, which we are unable to realize on a computer. The complexity of brain is further compounded by the fact that computing takes place not only inside the cell body, or soma, but also outside in the dendrites and synapses. Storage: - Neural networks store information in the strengths of the interconnections. In a computer, information is stored in the memory,...
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