Research On Time-Series Prediction Model Based On Process Neural Networks

Time series prediction is forecast to be things of the past and present observational data, structural changes in the time-series model and use it to reflect in the development process, the direction and trends, extrapolation or extended, which is expected to predict a period of time , the forecast is an important component of the field, in engineering and scientific research. Process neural networks expression system can be more time-varying input signals in the polymerization of space and time course of the cumulative effect, it can directly process data, and has a time-varying function in the continuity of space and to meet Lipschitz condition functional approaching capacity, as well as the Turing machine equivalent computing power, which makes the process neural network in the solution of practical problems of a wide range of adaptability and flexibility.This paper has been forecast for timing problems with the theories, methods, models summary, the present method of the problems and difficulties of the analysis, and forecasting model based on the timing and process of RBF neural network, a combination of new nonlinear forecasting methods. The method means that the forecast results of a single forecast, as RBFPNN input, the actual value of the historical data network as the expectations of output, so to avoid a general linear combination of forecasting methods identified in the various weight of the complex, could cover the practical problems of both linear and non-linear, integrated use of a single prediction method to provide the information and improve the prediction accuracy. Finally, the methods used in this projection airline forecast in the number of passengers, and achieved satisfactory results.Nonlinear time series against the chaotic time series, the paper from the reconstruction phase space theory, determine the phase space of embedding dimension and a variety of time delay methods. Chaos on the time sequence of the identification method, and chaotic phase space forecasting model were discussed in detail, and in accordance with phase space reconstruction process neural network technology and the technical principles for the combination of the two, and thus proposed a phase-space neural network structure and the process of combining chaotic time series forecasting methods. And the sunspot forecasting as an example verifies the effectiveness of the algorithm.Against the prediction of the state Markov chain prediction transfer, a process based on discrete neural network (DPNN) transfer the equivalent state forecasting methods and models were created, this paper explored the DPNN Markov model under certain conditions for the transfer of state system Description of equivalence relations. For arbitrary Markov chain, this paper proposed the equivalent DPNN Construction methods and state Markov chain transition probability under the conditions of the network-weight-matrix algorithm, the simulation results demonstrate the effectiveness of the method.