Self-organized path constraint neural network: Structure and algorithm

Abstract

Due to its flexibility and self-determination, self-organized learning neural network(NN) has been widely applied in many fields. Meanwhile, it has a well trend to develop. In our research, we find that structural equation modeling (SEM) may be reconstructed into a self-organized learning neural network, but the algorithm of NN need to be improved. In this paper, we first present an improved partial least square (PLS) algorithm in SEM using a suitable iterative initial value with constraint of unit vector. Then we propose a new self-organized path constraint neural network(SPCNN) based on SEM. Furthermore, we give the topology structure of SPCNN, describe the learning algorithm of SPCNN, including common algorithm and algorithm with a suitable initial weights value, and elaborate the function of SPCNN. © Springer-Verlag Berlin Heidelberg 2006.