本文選題：生物地理學優(yōu)化算法 + 時間序列預測�。� 參考：《蘭州交通大學》2017年碩士論文

【摘要】：BBO(Biogeography-based Optimization,生物地理學優(yōu)化)算法是一種新型的基于群體智能的進化算法,因其良好的全局尋優(yōu)能力和魯棒性,備受國內外眾多研究者的關注,目前已廣泛應用于現(xiàn)實生活中的優(yōu)化問題中。時間序列預測與人們生活中許多實際應用息息相關,一直以來都是廣大專家學者們研究的熱點和難點。如何提高工程應用中時間序列的預測精度具有重要的理論價值與實際應用價值�；贓LM(Extreme Learning Machine,極限學習機)的預測模型已被廣泛應用于工程應用中,并取得了良好的預測性能,ELM方法與優(yōu)化算法的結合理應是提升時間序列預測精度的有利候選者。針對時間序列預測,將BBO優(yōu)化算法用于ELM網(wǎng)絡結構及其參數(shù)的優(yōu)化選取,提出基于BBO算法優(yōu)化ELM的BBO-ELM自適應預測方法。主要研究內容有如下幾個方面:(1)研究BBO優(yōu)化算法的基本理論及其數(shù)學模型,把工程應用中的優(yōu)化問題轉化為基于BBO優(yōu)化算法的數(shù)學模型,對該模型的優(yōu)化和具體實現(xiàn)進行深入研究,闡述BBO優(yōu)化算法與其他進化算法的異同點。簡述時間序列預測的基本概念及其建模方法,并在標準混沌時間序列上,對ELM方法的預測性能進行測試,測試結果表明ELM方法對非線性時間序列具有良好的預測能力。(2)針對如何選取時間序列中有效的和必需的歷史信息的關鍵點,研究基于BBO優(yōu)化算法與ELM方法結合的預測模型,優(yōu)化ELM網(wǎng)絡的輸入變量選擇,同時,還通過BBO優(yōu)化選取ELM的隱含層節(jié)點數(shù)目及其參數(shù)(連接權值、偏置和激活函數(shù))、正則化參數(shù),得到BBO-ELM方法。在所提出方法的基礎上,引入余弦遷移模型和混沌映射理論分別對其進行改進,得到MCBBO-ELM方法和CBBO-ELM方法。將上述方法與現(xiàn)有的GA-ELM等方法在同等條件下應用于Mackey-Glass混沌時間序列預測中并進行比較,實驗結果顯示BBO-ELM的預測性能得到明顯提升,驗證其有效性。(3)將所提出方法應用于網(wǎng)絡流量預測、風電功率預測和交通流量預測實例中,實驗結果表明,在同等條件下本文方法的收斂速度和預測精度優(yōu)于對比方法,證實所提出方法的有效性和魯棒性。
[Abstract]:BBO(Biogeography-based optimization (biogeographic optimization) algorithm is a new evolutionary algorithm based on swarm intelligence. Because of its good global optimization ability and robustness, many researchers at home and abroad pay close attention to it. At present, it has been widely used in real life optimization problems. Time series prediction is closely related to many practical applications in people's lives and has always been a hot and difficult point for experts and scholars. How to improve the prediction accuracy of time series in engineering applications has important theoretical value and practical application value. The prediction model based on ELM(Extreme Learning machine (extreme learning machine) has been widely used in engineering applications, and the combination of good prediction performance and optimization algorithm should be a favorable candidate to improve the prediction accuracy of time series. For time series prediction, the BBO optimization algorithm is applied to the optimization of ELM network structure and its parameters, and a BBO-ELM adaptive prediction method based on BBO algorithm to optimize ELM is proposed. The main research contents are as follows: (1) the basic theory and mathematical model of BBO optimization algorithm are studied, and the optimization problem in engineering application is transformed into a mathematical model based on BBO optimization algorithm. The optimization and implementation of the model are deeply studied, and the similarities and differences between the BBO optimization algorithm and other evolutionary algorithms are expounded. The basic concept of time series prediction and its modeling method are briefly introduced, and the prediction performance of ELM method is tested on the standard chaotic time series. The test results show that the ELM method has a good ability to predict nonlinear time series. Aiming at the key points of how to select the effective and necessary historical information in the time series, a prediction model based on the combination of BBO optimization algorithm and ELM method is studied. The input variable selection of ELM network is optimized. At the same time, the number of hidden layer nodes and their parameters (connection weight, bias and activation function, regularization parameters) of ELM are optimized by BBO, and the BBO-ELM method is obtained. On the basis of the proposed method, the cosine migration model and the chaotic mapping theory are introduced to improve them, and the MCBBO-ELM method and the CBBO-ELM method are obtained. The above methods are applied to the prediction of Mackey-Glass chaotic time series under the same conditions with the existing methods such as GA-ELM, and the experimental results show that the prediction performance of BBO-ELM is improved obviously. The proposed method is applied to network flow prediction, wind power prediction and traffic flow prediction. The experimental results show that the convergence speed and prediction accuracy of the proposed method are better than that of the contrast method under the same conditions. The effectiveness and robustness of the proposed method are verified.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;O211.61

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