New entropy learning method for neural network

An entropy penalty term is used to steer the direction of the hidden node´s activation in the process of learning. A state with minimum entropy means that nodes are operating near the extreme values of the Sigmoid curve. As the training proceeds, redundant hidden nodes´ activations are pushed towards their extreme value, while relevant nodes remain active in the linear region of the Sigmoid curve. The early creation of redundant nodes may impair generalisation. To prevent the network from being driven into saturation before it can really learn, an entropy cycle is proposed to dampen the early creation of such redundant nodes