A Parameter-Separable Learning Algorithm for Multilayer Feedforward Neural Networks
一种多层前馈网参数可分离学习算法

Most learning algorithms simultaneously process all the parameters of the neural network. This often needs a large amount of time if the neural network is large. Many neural metworks, such as perceptrons, radial basis function networks ,general regression neural networks and fuzzy neural networks, may be regarded as a kind of multilayer feedforward neural networks. Their input-output mapping can be expressed as a linear combination of variable basis functions. Parameters of these neural networks can also be divided into two kinds. nonlinear arameters of the variable basis function and the coefficients of the linear combination. Based on this a parameter-separable learning algorithm is proposed. Simulation results show that the algorithm can accelerate the learning process and improve the approximating quality of the neural network.