REVISED GMDH-TYPE NEURAL NETWORKS WITH RADIAL BASIS FUNCTIONS AND THEIR APPLICATION TO MEDICAL IMAGE RECOGNITION OF STOMACH

摘要

In this paper, the revised Group Method of Data Handling (GMDH)-type neural networks with radial basis functions are proposed. This algorithm has been developed based on the basic GMDH-type neural networks which have both characteristics of the GMDH, the conventional multi-layered neural networks and the multi-layered radial basis function networks. The GMDH-type neural networks are automatically organized by using the heuristic self-organization method proposed by A.G. Ivakhnenko. In the GMDH-type neural networks, many types of neurons such as the sigmoid function type, the radial basis function type, the high order polynomial type and the linear function type, are used and these neurons are automatically selected so as to fit the complexity of the nonlinear system by using an error criterion defined as Akaike's Information Criterion (AIC). The revised GMDH-type neural networks with radial basis functions proposed in this paper can identify a complex nonlinear system more accurately compared with the conventional radial basis function networks. Revised GMDH-type neural networks with radial basis functions are applied to the medical image recognition of the stomach and it is shown that this algorithm is very useful method for the medical image recognition.