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Training in feedforward networks is adaptation of weights of network which makes weight initialization an important factor in determining the training speed in network. This work aims at proposing a new weight initialization technique, wherein the weights are initialized in the useful range in threshold function. It further initializes weights to each hidden node in a region which alternately contracts and expands statistically. The proposed weight initialization is compared to the conventional random weight initialization. The proposed weight initialization is expected to perform better than the random weight initialization.