Research on double parallel feedforward process neural networks and its application

To solve the problems of slow convergence speed and low accuracy of the multilayer feedforward process neural networks, a double parallel feedforward process neural networks model is proposed. By introducing a set of appropriate orthogonal basis functions into the input space, the input functions and the weight functions are expanded under the orthogonal basis functions, and the time aggregation operation of the process neurons is simplified by using the orthogonality of the basis functions. The corresponding learning algorithm is given and the effectiveness of this method is proved by the prediction of exhaust gas temperature in aircraft engine condition monitoring.