IDENTIFYING OPTIMAL NUMBER OF ORTHONORMALISATION IN LEARNING ALGORITHM USING WEATHER FORECASTING

Abstract

Artificial neural networks are more powerful than any other traditional expert system in the classification of patterns, which are non linear and in performing pattern classification tasks because they learn from examples without explicitly stating the rules. Multilayered feed forward neural networks possess a number of properties, which make them particularly suited to complex problems. Their applications to some real world problems are hampered by the lack of a training algorithm which finds a globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optimization procedures, which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. In this study, conversion of random weights into orthonormal weights (orthonormalisation) in feed forward network algorithm is proposed and also optimal number of orthonormalisation of weight is evaluated. Orthonormal weight based artificial neural network algorithms not only succeed in its task but also outperforms the genetic algorithm. This success comes from orthonormal weights instead of random and genetic weights. This paper has documented the evolution and ultimate success of this algorithm using weather forecasting data.