Application Of An Artificial Neural Network Essay

We have the previous workload information so we trained that workload information. First we normalize the value of the workload information by using the formula Where, M = is the maximum value along the particular column, X= is the maximum value along the particular column, Q= is the original value. Q’=is the normalized value. After Normalized the value we design an artificial neural network. 3.1 Artificial neural network In this structure of ANN we use the one input layer, one or more hidden layer, and one output layer. That structure we call MLP (multiple layer perceptron). On the input layer we use input layer 5 neurons. On the first hidden layer we are using the 5 neurons. And on the second hidden layer we use the 10 neurons. On the output layer we use the one neurons that predict the future load.NOW we train the workload information by using the Artificial neural network i.e. MLP structure. To Train the workload information aim is to find the set of weight values that will cause the output from the ANN to match to the target values as closely as possible. There several issues are arising when we train the neural network. First is selecting the number of hidden layer and neurons how much are used on the hidden layer. Second is to avoid the local minima and finding the globally optimal solution. To training the work load information first we need to divide the workload information. How much worklo ad information is used to train? How much workload information forShow MoreRelatedAn Evolving Diagnostic Decision Support System769 Words   |  4 PagesC.: Introduction to artificial intelligence. ISBN 048624864X,Courier Corporation (1985).† 5. SuzukiK.: Artificial neural networks: methodological advances and biomedical applications. InTech, ISBN-13: 9789533072432( 2011).† 6. Lingras P. J.: Rough neural network. In: Proc. of the 6th Int. 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