本文選題：作物模型 + 基因表達(dá)式編程�。� 參考：《安徽農(nóng)業(yè)大學(xué)學(xué)報(bào)》2017年01期

【摘要】：針對(duì)常用的回歸和神經(jīng)網(wǎng)絡(luò)作物建模方法存在的輸出單一、參數(shù)優(yōu)化困難和預(yù)測(cè)精度不足等問(wèn)題,利用基因表達(dá)式編程優(yōu)異的全局搜索能力和RBF神經(jīng)網(wǎng)絡(luò)多輸出任意非線性函數(shù)逼近特點(diǎn),設(shè)計(jì)了1種GEP優(yōu)化的RBF多輸出模型算法GEP-RBF。以水稻和番茄的5個(gè)關(guān)鍵環(huán)境因子為輸入、以葉片CO_2交換率和蒸騰速率為輸出,進(jìn)行建模驗(yàn)證。結(jié)果顯示,在預(yù)測(cè)的均方根誤差指標(biāo)上,GEP-RBF模型與GA-RBF和RBF相比,水稻的CO_2交換率和蒸騰速率分別降低了約28.4%、38.0%和89.9%、62.8%,番茄的CO_2交換率和蒸騰速率則分別降低了約56.9%、48.4%和75.3%、67.1%;在多輸出結(jié)果的平衡性指標(biāo)上,相比GA-RBF和RBF,GEP-RBF模型提高了約16.4%~77.4%。結(jié)果表明,GEP-RBF模型具有良好的預(yù)測(cè)精度和多輸出平衡性,是一種有效的作物生長(zhǎng)建模方法。
[Abstract]:In order to solve the problems of single output, difficulty in parameter optimization and low precision of prediction, the commonly used methods of regression and neural network for crop modeling are discussed. Taking advantage of the excellent global searching ability of gene expression programming and the characteristic of multi-output arbitrary nonlinear function approximation of RBF neural network, a GEP optimized RBF multi-output model algorithm GEP-RBF is designed. Five key environmental factors of rice and tomato were taken as input and leaf CO_2 exchange rate and transpiration rate as output. The results show that the GEP-RBF model is compared with GA-RBF and RBF on the root-mean-square error index. The CO_2 exchange rate and transpiration rate of rice decreased about 28.480% and 89.9%, respectively, while the CO_2 exchange rate and transpiration rate of tomato decreased about 56.9% and 75.3%, respectively, compared with GA-RBF and RBFGP-RBF model, the CO_2 exchange rate and transpiration rate of tomato decreased by about 16.4% and 77.4%, respectively. The results show that the GEP-RBF model has good prediction accuracy and multi-output balance. It is an effective modeling method for crop growth.
【作者單位】： 安徽農(nóng)業(yè)大學(xué)信息與計(jì)算機(jī)學(xué)院;
【基金】：農(nóng)業(yè)部國(guó)際科技合作項(xiàng)目(948計(jì)劃,2015-Z44和2016-X34) 安徽省自然科學(xué)基金(1508085MF110) 安徽省科技攻關(guān)項(xiàng)目(1501031102)共同資助
【分類號(hào)】：S311;S126
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本文編號(hào)：1868425