t 1 f denotes an output function wi denotes a synapse coupling coefficient weighting coefficient of an ith input yi t denotes the ith input value at a time t u03b8 denotes a threshold value and n denotes a number of hierarchical layers equ1 the output function f can be described by the following formula 2 where u03b5 denotes a non negative parameter which determines the non linearity of the output function f the formula 2 becomes a unit step function when u03b5 u2192 0 the output value of each neuron unit takes a binary value 1 or 0 and the formula 2 describes the excitation or inhibition state of the neuron the output value of each neuron takes a value from 0 to 1 in other cases the most notable characteristic of the neural network that is the learning function and the self organization are based on the changes in the synapse coupling coefficient w weighting of the coupling between the neurons and the threshold value u03b8 but mainly the synapse coupling coefficient w the synapse coupling coefficient w can be obtained from the following formulas 3 where k is a parameter satisfying 0 lt k lt 1 c.