Artificial Neural Networks
PAPER ON
ARTIFICIAL NEURAL NETWORKS
ABSTRACT
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
ARTIFICIAL NEURAL NETWORKS
ABSTRACT
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
Bibliography: • IEEE Computers, Oct. 1996. • Artificial Neural Networks, B.Yegnanarayana. • W.Ashby, Design for a Brain, New York: Wiley and Sons, 1952 • G.Burke, ‘Neural Networks: Brainy way to trade?’, Futures, August. 1992.