Algorithm of neural-network about solving nonlinear least squares adjustment by parameters

Study on solving nonlinear least squares adjustment by parameters is one of the most important and new subjects in modern surveying and mapping field . Many researchers have done a lot of work and gained some solving methods. These methods mainly include iterative algorithms and direct algorithms mainly. The former searches some methods of rapid convergence based on which surveying adjustment is a kind of problem of nonlinear programming. Among them the iterative algorithms of the most in common use are the Gauss-Newton method, damped least quares, quasi-Newton method and some mutations etc. Although these methods improved the quantity of the observation results to a certain degree, and increased the accuracy of the adjustment results, what we want is whether the initial values of unknown parameters are close to their real values. Of course, the model of the latter has better degree in linearity, that is to say, they nearly have the meaning of deeper theories researches. This paper puts forward a kind of method of solving the problems of nonlinear least squares adjustment by parameters based on neural network theory, and studies its stability and convergency. The results of calculating of living example indicate the method acts well for solving parameters problems by nonlinear least squares adjustment without giving exact approximation of parameters.