Artificial neural networks seen to be useful in many applications in recent times like prediction, classification, recognition,translation and many more. The current example is an application of simple ANN in predicting the output given the input numbers.
We will be considering an example of an machine which takes in input A, B, C and produces an Output. The example includes training the artificial neural network with set of data(training data) and testing with a different set of data which network wasn’t fed before(test data). Data in this case, is collected from experiments with different experimental settings. Example, given input settings A=1,B=1,C=1 produces an output =1, number of runs has to be performed with different experimental settings to get the data. Acquired data has to be divided into two sets- training set( used for training the neural network, test set- used for testing the performance of trained neural network) with ratio of train to test data generally being 80:20.
ANN is similar to human neural network consisting of connected neurons processing information. ANN architecture is shown below with three layers, input layer- layer through which input information is fed, hidden layer- layer connecting input and output layers processing the information, output layer-layer delivering the output.

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