Neural Network Learning Algorithms And Its Application

Artificial neural network is a kind of information processing system which canimitate the human brain structure and function, also can raise the level of intelligentinformation processing. It is a new edge and cross-disciplinary subject, compared withprevious, it has a great progress in the aspects of theory, models, algorithms etc, but thereis no fundamental breakthrough, there are a lot of gaps need to work hard to explore andresearch. The existing neural network system also has many problems, such as thelearning algorithm problem of multi-layer perceptron, false attract problem of Hopfieldneural network system, and so on, all of these problems are urgently need to solve.Therefore, for different practical applications problem, it is an important subject to set updifferent index analysis system and learning algorithm of neural network systemdevelopment.This paper discusses the neural network learning algorithm and its application. Usingthe advantages of the trust region algorithm in optimization theory, it improves someshortcomings of the traditional algorithm, so that neural network learning algorithm canbetter be applied to practice.First of all, based on the traditional BP (Back Propagation) algorithm, this paperimproves the algorithm and proposes a new algorithm. Then it does an experiment to testthe feasibility of the method.Secondly, based on the stability of the Hopfield neural network system, this paperimproves its learning algorithm and proves the convergence of the algorithm.Lastly, the improved Hopfield neural network algorithm is applied to the parametersestimation of time series ARMA model. The new algorithm improves the precision of theparameters estimation.In the paper, it uses the MATLAB language to realize the construction of the neuralnetwork and its training. It displays that the MATLAB programming is simple, fast,convenient and practical.