本文選題：時(shí)間序列預(yù)測　切入點(diǎn)：自組織映射　出處：《蘭州交通大學(xué)》2015年碩士論文

【摘要】：近年來,自組織映射(Self-organizing Map,SOM)神經(jīng)網(wǎng)絡(luò)在時(shí)間序列預(yù)測方面的應(yīng)用逐漸受到國內(nèi)外研究者的廣泛關(guān)注,已成為具有重要的理論與應(yīng)用價(jià)值的研究熱點(diǎn)。作為一種非監(jiān)督競爭學(xué)習(xí)型神經(jīng)網(wǎng)絡(luò),SOM神經(jīng)網(wǎng)絡(luò)的構(gòu)造簡單直觀,其聯(lián)想記憶技術(shù)避免了傳統(tǒng)方法易陷入局部最優(yōu)的問題。本文研究了SOM神經(jīng)網(wǎng)絡(luò)的改進(jìn)方法在時(shí)間序列預(yù)測方面的應(yīng)用,以滿足現(xiàn)實(shí)應(yīng)用對預(yù)測精度的要求,為非監(jiān)督神經(jīng)網(wǎng)絡(luò)在時(shí)間序列預(yù)測方面的應(yīng)用擴(kuò)展了新的空間。本文的主要研究內(nèi)容包括如下幾個(gè)方面:(1)研究時(shí)間序列預(yù)測理論,以及SOM神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)和算法。將SOM神經(jīng)網(wǎng)絡(luò)的聯(lián)想記憶技術(shù)推廣到時(shí)域,矢量量化臨時(shí)聯(lián)想記憶(Vector Quantized Temporal Association Memory,VQTAM)建模技術(shù)可實(shí)現(xiàn)時(shí)間序列預(yù)測。(2)提出一類遞推SOM方法,包括遞推的自組織映射(Recursive Self-organizing Map,RecSOM)方法和適用于結(jié)構(gòu)化數(shù)據(jù)的結(jié)構(gòu)化數(shù)據(jù)自組織映射(Self-organizing Map of Structured Data,SOMSD)方法。遞推SOM方法利用上下文信息反映數(shù)據(jù)集的統(tǒng)計(jì)特性,其中,RecSOM方法用帶時(shí)延的反饋表現(xiàn)遞推的概念,SOMSD方法利用獲勝神經(jīng)元的網(wǎng)格坐標(biāo)表示上下文信息,更適用于結(jié)構(gòu)化數(shù)據(jù)。遞推SOM方法應(yīng)用于交通流預(yù)測實(shí)例,并在同等情況下與其他預(yù)測方法進(jìn)行對比,結(jié)果驗(yàn)證提出的方法是可行的、有效的。(3)在VQTAM建模技術(shù)的基礎(chǔ)上,提出一類基于SOM神經(jīng)網(wǎng)絡(luò)的局部自回歸(Auto-regressive,AR)方法。給出具有多個(gè)局部線性AR模型的AR-SOM方法,基于前K個(gè)獲勝神經(jīng)元用權(quán)值代替輸入向量建立單一時(shí)變局部AR模型的K-SOM方法,以及在完成數(shù)據(jù)向量聚類的同時(shí),更新多個(gè)局部AR模型系數(shù)的LLM(Local Linear Map)-SOM方法。相對于全局模型,所提出的方法能夠靈活給出有效的監(jiān)督神經(jīng)結(jié)構(gòu),降低了計(jì)算復(fù)雜度。將其應(yīng)用于不同的混沌時(shí)間序列預(yù)測典型實(shí)例中,進(jìn)一步還將其應(yīng)用于網(wǎng)絡(luò)流預(yù)測實(shí)例和視頻流預(yù)測的實(shí)例中,在同等條件下與已有方法比較,實(shí)驗(yàn)結(jié)果表明,所提出的方法能有效改善預(yù)測精度,且性能更好,驗(yàn)證了其有效性與應(yīng)用潛力。
[Abstract]:In recent years, the application of Self-Organizing Map SOM (SOM) neural network in time series prediction has been paid more and more attention by researchers at home and abroad, and has become an important research hotspot in theory and application.As an unsupervised competitive learning neural network, SOM neural network has a simple and intuitive structure, and its associative memory technology avoids the problem that traditional methods are prone to local optimum.In this paper, the application of the improved method of SOM neural network in time series prediction is studied in order to meet the requirement of prediction accuracy in practical applications and to extend a new space for the application of unsupervised neural networks in time series prediction.The main contents of this paper are as follows: 1) study the theory of time series prediction and the structure and algorithm of SOM neural network.In this paper, the associative memory technique of SOM neural network is extended to the time domain. A kind of recursive SOM method is proposed, which can be used to predict the time series by using vector quantization (VQ) Quantized Temporal Association memory (VQTAM) modeling technique.It includes the recursive Self-organizing mapping (RecSOM) method and the self-organizing Map of Structured data (SOMSD) method for structured data.The recursive SOM method uses the context information to reflect the statistical characteristics of the dataset. The RecSOM method uses the recursive concept of feedback with time delay to represent the context information using the grid coordinates of the winning neurons, which is more suitable for structured data.The recursive SOM method is applied to traffic flow forecasting example and compared with other forecasting methods in the same situation. The results show that the proposed method is feasible and effective on the basis of VQTAM modeling technology.A class of local autoregressive autoregressive ARs based on SOM neural network is proposed.The AR-SOM method with multiple local linear AR models is presented. The K-SOM method of establishing a single time-varying local AR model based on the weights of the first K winning neurons instead of the input vector is presented. At the same time, the clustering of the data vectors is completed.LLM(Local Linear Map)-SOM method for updating the coefficients of multiple local AR models.Compared with the global model, the proposed method can provide an effective supervised neural structure flexibly and reduce the computational complexity.It is applied to different typical examples of chaotic time series prediction, and it is also applied to network flow prediction examples and video stream prediction examples. The experimental results show that, under the same conditions, the proposed method is compared with existing methods.The proposed method can effectively improve the prediction accuracy and the performance is better. The validity and application potential 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】：2015
【分類號(hào)】：U495

【共引文獻(xiàn)】

相關(guān)期刊論文 前1條

1 黃杰;李軍;郭翔;;遞推SOM神經(jīng)網(wǎng)絡(luò)在短時(shí)交通流預(yù)測中的應(yīng)用[J];公路;2015年04期

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本文編號(hào)：1710864