A Method to Determine the Structure and Parameters of BP Neural Network from Knowledge

Neural networks have been widely used in many fields of science and engineering for its high level learning abilities But the further application is difficult because the weights and the thresholds of the neural network cannot be explained and understood at all Many research works on extracting rules from the neural network have been carried out for these issues However, the methods remain more complicated themselves and have the inferior understandabilities for extracted rules To overcome the drawbacks of the previous methods, a new method for determining the structure and the parameters of a general BP neural network from the knowledge is proposed Using the knowledge derived from the samples, the size and the parameters of the network can be determined The advantage of the proposed method is both the structure of the network and the traditional BP learning algorithm employed in the network need not to be changed Using this approuch, the network can approximately track the outputs without learning As a result, the learning time of the network can be shortened to some degree, and the significance of the weights and the thresholds can then be explained This is very helpful to extract fuzzy rules from the neural network directly The simulation results prove the validity of the proposed method