Abstract: The dynamic model of passive dynamic walking robot is given.BP(backpropagation) neural network(NN) is used to estimate the stable fixed point,and the estimated fixed point is taken as initial value for Newton-Raphson iteration to search the stable fixed point.This method solves the problems of low successful search rate and long search time caused by the improper initial value for the walking model which is not the simplest in previous study.Small variation of parameters leads to small variation of fixed points when acquiring training samples.When calculating fixed points of two successive samples,only one parameter has a small variation.The fixed point obtained with Newton-Raphson iteration is used as initial value for the next sample.1000 groups of parameters are generated randomly to test the NN performance.The results show that this method increases the successful search rate of stable fixed points significantly and shortens the search time greatly.