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基于粒子群尋優(yōu)的支持向量機(jī)番茄紅素含量預(yù)測
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國家自然科學(xué)基金資助項(xiàng)目（30972036）


Lycopene Content Prediction Based on Support Vector Machine with Particle Swarm Optimization
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    應(yīng)用支持向量機(jī)（SVM）通過色差值對番茄果實(shí)番茄紅素含量預(yù)測進(jìn)行建模,，解決預(yù)測過程受影響因素多,、參數(shù)互相關(guān)聯(lián),、難以建立精確模型問題,。為提高預(yù)測精度,，將SVM參數(shù)選擇和輸入變量的選取看作組合優(yōu)化問題,，通過赤池信息準(zhǔn)則（AIC）構(gòu)造組合目標(biāo)優(yōu)化函數(shù),，采用粒子群算法（PSO）進(jìn)行目標(biāo)函數(shù)搜索,，提高了搜索效率,。對采后儲藏不同成熟度番茄進(jìn)行的測量表明，所提預(yù)測建模算法在番茄紅素的預(yù)測中具有良好的性能,，為番茄紅素的便捷,、無破壞性測量提供了一種方法。

    Abstract:

    Color-difference was presented to assess the lycopene content conveniently and non-destructively. Due to excessive affecting factors and strong correlation among the parameters in the process, the support vector machine (SVM) was used to set up the predict model. The selection and simplification of the feature parameters was discussed. A compound optimal objective function based on Akaike information criterion (AIC) was constructed. The particle swarm optimization (PSO) algorithm was used to search the optimal value of the objective function and enhance the efficiency. The predictable method had good performance in assessing the lycopene content of different maturity stages.

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劉偉,王建平,劉長虹,應(yīng)鐵進(jìn).基于粒子群尋優(yōu)的支持向量機(jī)番茄紅素含量預(yù)測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2012,43(4):143-147,155. Liu Wei,Wang Jianping, Liu Changhong,Ying Tiejin. Lycopene Content Prediction Based on Support Vector Machine with Particle Swarm Optimization[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(4):143-147,155.

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  • 在線發(fā)布日期: 2012-04-18
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