本文關(guān)鍵詞： GDP CPI BP神經(jīng)網(wǎng)絡(luò)算法 主成分分析 粒子群算法 灰狼優(yōu)化算法 支持向量機(jī)　出處：《中北大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：國際經(jīng)濟(jì)競(jìng)爭(zhēng)越來越激烈,為保證我國在國際競(jìng)爭(zhēng)中立于不敗之地,必須保證宏觀經(jīng)濟(jì)穩(wěn)定發(fā)展。GDP增長(zhǎng)率反映了國家經(jīng)濟(jì)狀況的整體水平,CPI指數(shù)直接影響國民購買力。在制定宏觀經(jīng)濟(jì)政策時(shí),必須研究歷史數(shù)據(jù),從歷史數(shù)據(jù)中尋求它們之間的內(nèi)在關(guān)系,進(jìn)而為制定合適的方針政策提供指導(dǎo)。GDP增長(zhǎng)率與CPI指數(shù)的歷史數(shù)據(jù)具有復(fù)雜的時(shí)間序列性和非線性性。人工神經(jīng)網(wǎng)絡(luò)算法具有良好的非線性擬合能力,在處理非線性問題時(shí)得到了廣泛應(yīng)用。BP神經(jīng)網(wǎng)絡(luò)算法和SVM算法作為人工神經(jīng)網(wǎng)絡(luò)中兩種廣泛使用的算法,都具有良好的非線性擬合能力,但也存在不足。針對(duì)不足之處,本文做了以下幾方面的工作。1、針對(duì)BP神經(jīng)網(wǎng)絡(luò)算法容易陷入局部極小值,提出了PSO-BP模型算法。該方法主要利用PSO算法良好的全局尋優(yōu)能力對(duì)BP算法的權(quán)值和閾值進(jìn)行優(yōu)化,從而避免BP算法陷入局部極小值。2、SVM算法中參數(shù)的選擇直接影響模型的性能,所以如何選擇恰當(dāng)?shù)膮?shù)至關(guān)重要。文中利用GWO灰狼算法良好的全局尋優(yōu)能力對(duì)SVM的參數(shù)進(jìn)行尋優(yōu),從而提高SVM模型的預(yù)測(cè)精度。3、PCA主成分可以實(shí)現(xiàn)降維,并保留原始數(shù)據(jù)的絕大部分信息,文中用PCA算法對(duì)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行降維,來提高模型的預(yù)測(cè)精度。通過PCA-PSO-BP模型對(duì)GDP增速和CPI指數(shù)進(jìn)行擬合預(yù)測(cè),實(shí)驗(yàn)結(jié)果表明PCA-PSO-BP模型比PCA-BP模型和PSO-BP模型具有更高的擬合精度和更小的均方誤差。此外,文中也對(duì)PCA-GWO-SVM模型進(jìn)行了GDP增長(zhǎng)率和CPI指數(shù)的預(yù)測(cè)實(shí)驗(yàn)。結(jié)果表明PCA-GWO-SVM模型比PCA-SVM模型和GWO-SVM模型具有更高的擬合精度和更小的均方誤差。通過PCA-PSO-BP模型與PCA-GWO-SVM模型的實(shí)驗(yàn)結(jié)果對(duì)比發(fā)現(xiàn),PCA-GWO-SVM模型有更小的均方誤差。
[Abstract]:International economic competition is becoming fiercer and fiercer. In order to ensure that our country is in an invincible position in international competition, It is necessary to ensure that the steady development of the macro economy... GDP growth rate reflects the overall level of the national economic situation. CPI directly affects the purchasing power of the people. In formulating macroeconomic policies, we must study historical data. Looking for the interrelationship between them from historical data, The historical data of CPI index and GDP growth rate have complex time series and nonlinearity. The artificial neural network algorithm has good nonlinear fitting ability. In dealing with nonlinear problems, BP neural network algorithm and SVM algorithm are widely used as two widely used algorithms in artificial neural network. Both of them have good nonlinear fitting ability, but they also have some shortcomings. In this paper, the following work has been done. 1. Aiming at BP neural network algorithm is easy to fall into local minima, the PSO-BP model algorithm is proposed. This method mainly uses the good global optimization ability of PSO algorithm to optimize the weight and threshold of BP algorithm. In order to avoid the BP algorithm falling into the local minimum. 2SVM algorithm parameters selection directly affect the performance of the model, so how to select the appropriate parameters is very important. In this paper, the good global optimization ability of GWO gray wolf algorithm is used to optimize the parameters of SVM. In order to improve the prediction accuracy of SVM model. 3PCA principal component can achieve dimensionality reduction, and retain most of the information of the original data. In this paper, we use PCA algorithm to reduce the dimension of experimental data. The experimental results show that PCA-PSO-BP model has higher fitting accuracy and smaller mean square error than PCA-BP model and PSO-BP model. The experimental results of GDP growth rate and CPI exponent of PCA-GWO-SVM model show that PCA-GWO-SVM model has higher fitting precision and smaller mean square error than PCA-SVM model and GWO-SVM model. The experimental results of PCA-PSO-BP model and PCA-GWO-SVM model show that the PCA-GWO-SVM model has higher fitting accuracy and smaller mean square error than PCA-SVM model and GWO-SVM model. The results show that the PCA-GWO-SVM model has smaller mean square error.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F124;F726;TP183

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