unknown parameters can be trained using for example gradient descent method quasi newton method or generation algorithm see nocedal jorge and wright stephen j 1999 numerical optimization springer verlag contents and disclosure of which is incorporated by reference as will be described in detail herein below the jnn uses the gradients e g the change rates of sales quantity to factors calculated from the backward neural network of the jnn e g as shown in fig4 in this manner key parameters are obtained such as customer preference which parameter information could have only been retrieved from prior art surveys 3 competition process simulation 30 after computing these key parameters via the jnn the configured jnn 100 is used to perform a simulation and produce a final solution such as an optimization of the price of channels in competition or the observing of resulting effects of different price setting and channel coordination strategies in one embodiment the judge neural network is a type of neural network and as shown in the example node a of the jnn portion 120 shown in fig2 the jnn includes three layers an input layer at least one hidden layer and an output layer it differs from artificial neural network ann configurations in how the data from the previous layer propagates to the next layer however in the identification procedure jnn is like a special ann where the data propagating to the next layer will keep the same in some range due to the piecewise linear property of jnn this configuration can save computing load in the backward propagation.