Self-organized Path Constraint Neural Network: Structure and Algorithm

Due to its flexibility and self-determination, Self-organized learning neural network has been widely applied in many fields; meanwhile, it has a well trend to develop. Structural equation model (SEM) may be reconstructed into a self-organized learning neural network, but the algorithm of SEM 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. Next we propose a new self-organized path constraint neural network(SPCNN) based on SEM; then give the topology structure of neural network, describe algorithm of learning, including common algorithm and algorithm with a suitable initial weights value, explain the mechanism and function of network.