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Analysis and Prediction of Temperature using Statistical Artificial Neural Network

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Author(s): Parag Kadu | Kishor Wagh | Prashant Chatur

Journal: International Journal of Computer Science and Management Studies
ISSN 2231-5268

Volume: 12;
Issue: 02;
Start page: 117;
Date: 2012;
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Keywords: Multilayer Perceptron Neural Network | Gradient

ABSTRACT
This paper is about producing a prediction system by usingartificial neural methods that will forecast temperature. Thispaper is based on three objectives. First, study of temperature andgathers all knowledge regarding the weather this is particularlystudied in analysis part of the paper. Second, gather allknowledge about artificial neural network methods. Implementmultilayer perceptron neural network with gradient descent(backpropagation), BFGS, conjugate gradient training algorithm andwill analyze the performance of all. Lastly, achieve an objectiveof developing a temperature prediction system. The generalfinding is that with BFGS algorithm, with multilayer perceptronmodel perform well with less prediction error and more accuracythan gradient descent and conjugate gradient, thus used fortemperature prediction. To implement this project we make useof statistica software which provides the functionality calledstatistica artificial neural network(SANN) which is used here fortemperature prediction and heavy weather software is used fordata gathering.
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