Procedure neural networks with supervised learning
Liang Jiuzhen, Zhou Jiaqing · 2004
A novel neural network model, the procedure neural network (PNN) is proposed, in which input term is associated with a procedure. Two forms of procedure neural networks are constructed. One is procedure neural network expanded on certain base functions, the other is procedure neural network based on projective combination, and they are equivalent to each other in structure. For the later procedure neural networks the continuity theorem, continuous functional approximation theorem and computing capability theorem are presented. Selection strategies of base functions and time aggregations are specially discussed. Supervised learning algorithm for training of procedure neural networks is provided. Finally, an application example, which is adaptive to the case of procedure neural networks, is simulated.