Abstract
Traditional learning methods focus on the training of neural networks. However robustness or fault tolerance or sensitivity of its input to output which plays an important role while designing a neural network are not considered appropriately. Purpose of this paper is the optimization of various feed forward artificial neural network (FFANN) architectures with hyper-parmeter selection and using proposed cost function AvgNew.This paper, has proposed a cost function which when applied to TDFFANN(tailored deep feed forward artificial neural network) and other FFANN architectures makes it more fault-tolerant, sensitive or robust. Result obtained using variants of MLP(Multi Layer Perceptron) and FFANN(feed forward artificial neural network) proves AvgNew to be efficient.
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