07-09-2012, 10:11 AM
Neural Network Learning Paradigm with Applications.
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Introduction:
The term neural network was traditionally used to refer to a network of biological neural. The modern usage of this network often refers to artificial neural network which is composed of neural network.
Neural Networks are a different paradigm for computing: von Neumann machines are based on the processing/memory abstraction of human information processing.
neural networks are based on the parallel architecture of animal brains.
Topic’s Desctiption
A survey of journal articles on neural network business applications published between 1988 and 1995 indicates that an increasing amount of neural network research is being conducted for a diverse range of business activities. The classification of literature by (1) year of publication, (2) application area, (3) problem domain, (4) decision process phase, (5) level of management, (6) level of task interdependence, (7) means of development, (8) corporate/academic interaction in development, (9) technology integration, (10) comparative study, (11) major contribution, and (12) journal provides some insights into the trends in neural networks research. The implications for neural networks developers/researchers and suggestions on future research areas are discussed.
Pros/cons of relevant topic
Neural networks imitate the brain's ability to sort out patterns and learn from trial and error, discerning and extracting the relationships that underlie the data with which it is presented. Most neural networks are software simulations run on conventional computers. In neural computers, transistor circuits serve as the neurons and variable resistors act as the interconnection between axons and dendrites (see nervous system ). A neural network on an integrated circuit , with 1,024 silicon "neurons," has also been developed. Each neuron in the network has one or more inputs and produces an output; each input has a weighting factor, which modifies the value entering the neuron. The neuron mathematically manipulates the inputs, and outputs the result. The neural network is simply neurons joined together, with the output from one neuron becoming input to others until the final output is reached. The network learns when examples (with known results) are presented to it; the weighting factors are adjusted—either through human intervention or by a programmed algorithm—to bring the final output closer to the known result.
conclusion:
Neural networks are suitable for predicting time series mainly because of learning only from examples, without any need to add additional information that can bring more confusion than prediction effect. Neural networks are able to generalize and are resistant to noise. On the other hand, it is generally not possible to determine exactly what a neural network learned and it is also hard to estimate possible prediction error.
Final Thoughts
Clearly the term artificial neural networks encompass a great variety of different software packages with many different types of artificial neurons, network architectures, and learning rules. These different networks can, in turn, be applied to a diverse range of functions in everything from beer manufacturing to better understanding the properties of the biological brains on which they are based