本文選題：生物地理學(xué)優(yōu)化算法 + 時間序列預(yù)測　； 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：BBO(Biogeography-based Optimization,生物地理學(xué)優(yōu)化)算法是一種新型的基于群體智能的進(jìn)化算法,因其良好的全局尋優(yōu)能力和魯棒性,備受國內(nèi)外眾多研究者的關(guān)注,目前已廣泛應(yīng)用于現(xiàn)實(shí)生活中的優(yōu)化問題中。時間序列預(yù)測與人們生活中許多實(shí)際應(yīng)用息息相關(guān),一直以來都是廣大專家學(xué)者們研究的熱點(diǎn)和難點(diǎn)。如何提高工程應(yīng)用中時間序列的預(yù)測精度具有重要的理論價值與實(shí)際應(yīng)用價值�；贓LM(Extreme Learning Machine,極限學(xué)習(xí)機(jī))的預(yù)測模型已被廣泛應(yīng)用于工程應(yīng)用中,并取得了良好的預(yù)測性能,ELM方法與優(yōu)化算法的結(jié)合理應(yīng)是提升時間序列預(yù)測精度的有利候選者。針對時間序列預(yù)測,將BBO優(yōu)化算法用于ELM網(wǎng)絡(luò)結(jié)構(gòu)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(yīng)預(yù)測方法。主要研究內(nèi)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(xué)模型,把工程應(yīng)用中的優(yōu)化問題轉(zhuǎn)化為基于BBO優(yōu)化算法的數(shù)學(xué)模型,對該模型的優(yōu)化和具體實(shí)現(xiàn)進(jìn)行深入研究,闡述BBO優(yōu)化算法與其他進(jìn)化算法的異同點(diǎn)。簡述時間序列預(yù)測的基本概念及其建模方法,并在標(biāo)準(zhǔn)混沌時間序列上,對ELM方法的預(yù)測性能進(jìn)行測試,測試結(jié)果表明ELM方法對非線性時間序列具有良好的預(yù)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(guān)鍵點(diǎn),研究基于BBO優(yōu)化算法與ELM方法結(jié)合的預(yù)測模型,優(yōu)化ELM網(wǎng)絡(luò)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(diǎn)數(shù)目及其參數(shù)(連接權(quán)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(chǔ)上,引入余弦遷移模型和混沌映射理論分別對其進(jìn)行改進(jìn),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(yīng)用于Mackey-Glass混沌時間序列預(yù)測中并進(jìn)行比較,實(shí)驗(yàn)結(jié)果顯示BBO-ELM的預(yù)測性能得到明顯提升,驗(yàn)證其有效性。(3)將所提出方法應(yīng)用于網(wǎng)絡(luò)流量預(yù)測、風(fēng)電功率預(yù)測和交通流量預(yù)測實(shí)例中,實(shí)驗(yàn)結(jié)果表明,在同等條件下本文方法的收斂速度和預(yù)測精度優(yōu)于對比方法,證實(shí)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(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;O211.61

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