本文關鍵詞： 植物纖維地膜 抗張強度 預測模型 支持向量機回歸 粒子群算法 正交試驗設計　出處：《農(nóng)業(yè)機械學報》2017年04期 　論文類型：期刊論文

【摘要】：為快速、準確地對生產(chǎn)過程中植物纖維地膜抗張強度進行預測,降低生產(chǎn)成本,提高原料利用率,以植物纖維地膜中試平臺為依托,基于粒子群算法(PSO)優(yōu)化支持向量機回歸(SVR)模型,結(jié)合正交試驗設計L25(56)方法,以纖維打漿度、施膠劑添加量、濕強劑添加量、地膜定量、混合比作為模型輸入?yún)?shù),以植物纖維地膜抗張強度為輸出進行模擬預測,并將模擬結(jié)果與SVR、BP、RBF智能算法模型進行對比分析。結(jié)果表明:PSO-SVR模型能夠較好地表達植物纖維地膜抗張強度與模型參數(shù)間的非線性關系,并能根據(jù)輸入?yún)?shù)快速準確地對植物纖維地膜抗張強度進行預測,測試集樣本中預測值與實際值間均方誤差、決定系數(shù)和均方根誤差為0.117 N2、0.915、0.342 N;與其他智能算法(SVR、BP、RBF)相比,PSO-SVR算法模型具有更高的適用性與穩(wěn)定性。研究結(jié)果可為生產(chǎn)過程中不同抄造工藝參數(shù)下植物纖維地膜抗張強度的在線監(jiān)控提供參考依據(jù)。
[Abstract]:For fast, accurate of plant fiber in the production process of plastic tensile strength prediction, reduce production cost, improve the utilization rate of raw material, the plant fiber film test platform based on particle swarm optimization (PSO) algorithm based on support vector machine regression (SVR) model, combined with orthogonal design L25 (56) method. The fiber beating degree, sizing agent dosage, wet strength agent addition, film quantitative mixing ratio as the model input parameters, the plant fiber film tensile strength were simulated as output, and the results of the simulation with SVR, BP, RBF intelligent analysis algorithm model. The results show that the PSO-SVR model can the expression of the nonlinear relationship between plant fiber film tensile strength and model parameters of the well, and can quickly and accurately according to the input parameters of plant fiber film tensile strength was predicted, and the actual value between the mean square prediction value in the sample test set Error, coefficient of determination and root mean square error is 0.117 N2,0.915,0.342 N; and other algorithms (SVR, BP, RBF) compared to the PSO-SVR algorithm and the applicability of the model has higher stability. The research results can provide reference basis for on-line monitoring of plant fiber film making process in the production process of different parameters under the tensile strength.

【作者單位】： 東北農(nóng)業(yè)大學工程學院;
【基金】：“十二五”國家科技支撐計劃項目(2012BAD32B02-5)
【分類號】：TB383.2;TP18

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1 陳國超;成新文;;PSO-SVR在果酒生物活性物質(zhì)預測中的應用[J];四川理工學院學報(自然科學版);2013年06期

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本文編號：1539390