Research On Prediction Model Of Ship Power System Based On Chaos Theory

Chaotic theory is an important part of non-linear theory which can grasp the characteristics from the seemingly irregular motion in the non-linear system such as the sensitivity to initial conditions, aperiodic variation and the existence of strange attractors. So it can excellently describe the motion law in the nonlinear system and open up new ideas for the research of predictive model.Two Parallel of ship power system is an important aspect of power system, its stability is directly related to the safety of operation of the ship, its stability analysis can provide the basis for the ship's stability control.Chaotic theory is applied to time series prediction in this thesis. On the basis of the analysis of research status at home and abroad about chaotic time series prediction model study, a comparatively systematic and deep study is carried out. The main research contents are as follows:(1) The theory of phase space reconstruction is studied. According to the mathematical definition the chaotic nature is discussed. The selection of two important parameters in the phase space reconstruction which are called embedding dimension and delay time is researched. Autocorrelation method, average displacement method, complex autocorrelation method, and mutual information method of delay time selection are realized. False nearest neighbours method and Cao's method of embedding dimension selection are realized. Application of these various methods to strike a parallel system of Marine Electric double embedding dimension and delay time, and to analyze the chaotic characteristics.(2) The method of chaotic characteristics identification is studied. Three important characteristic values of chaotic system which are called Lyapunov exponent, correlation dimension and Kolmogorov entropy are analyized. From the qualitative and quantitative points of view, the chaotic characteristics of time series is identifid. Power spectrum method and Poincare section method of qualitative identification are realized. Wolf method, small data method are realized. And strike the ship power system Lyapunov Two Parallel index.(3) Self-adaptive predictve model and RBF neural network predictve model are established. From the analysis of traditional linear predictive models, the inner limitations are found. Subsequently some commonly used chaotic time series predictive models such as the weighted one-rank local-region method and largest Lyapunov exponent method are researched, whose advantages are showen by the simulation experiment. At the same time aiming at the problems of learning disability in commonly used chaotic time series predictive models, the new predictive models such as self-adaptive and RBF neural network models are established. The predictive precision and accuracy is improved through simulation experiments and the valid predictive time is also extended. Chaotic series predictive model based on SVM is preliminarily discussed. And the various methods used in ships for more than two-machine parallel system-into-power, in the Matlab Simulation software platform to analyze the effectiveness and accuracy.(4) Under the existing research results in the laboratory, complete chaos test platform Three modules of phase space reconstruction, chaotic identification, and chaotic prediction are contained in the platform, which provides the test means for the follow-up study.