ELM Based Cerebellar Model Neural Networks And Lts Application In Forecasting Problems

This thesis presented a model called cerebellar model extreme leaming machine that apply to forecasting problems.Because of importance for decision making,forecasting problems always attract researchers.However,nowadays,with the high development of the social economy and technology,decision making has tend to more complicated,and forecasting problem has tend to high chaotic and nonlinear.The linear model in traditional forecasting method cannot meet the requirement of reality.Artificial neural network has been successfully applied to predict problems in many fields due to its nonlinear fitting ability and good generalization ability.However,the uncertainty of the model and dataset has not been considered in most of the previous researches which focus on point forecasting.The error of traditional point forecasting results is unavoidable because of the uncertainty.For a forecasting result with more reference value,researchers start to consider the uncertainty of the predict model and dataset,then constructs the predict intervals for forecasting result.This forecasting method called probabilistic forecasting.Extreme learning machine has been widely used in probabilistic forecasting because of the fast speed and good generalization ability,but the shortness of extreme leaming machine in terms of accuracy limits its performance.There is a model called cerebellar model neural network which has excellent nonlinear fitting ability,but its computation speed that based on gradient descent method still cannot meet the requirement of probabilistic forecasting.Base on the above,the main research contents of this thesis include:(1)A method,called cerebellar model extreme leaming machine(CELM),which is suitable for probabilistic forecasting and with higher accuracy and stability is present.CELM is used with Bootstrapping technique to implement the uncertainty and construct predict intervals.(2)In addition,wavelet decomposition is used in data preprocess for a higher accuracy.(3)Effective performance of the proposed model is validated by testing on two applications comes from financial field and industrial engineering field respectively,stock forecasting whose data comes from Taiwan securities exchange,and electric load forecasting which data comes from NingDe power system.