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基于改進(jìn)BP神經(jīng)網(wǎng)絡(luò)的排種器充種性能預(yù)測
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Performance of Seed-filling Process Based on Improved BP Neural Network
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    充種性能直接影響排種器排種質(zhì)量,應(yīng)用Matlab神經(jīng)網(wǎng)絡(luò)工具箱建立了排種器充種單粒率η1和空穴率η2的改進(jìn)BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型,。選取轉(zhuǎn)速n,、種子當(dāng)量直徑d、充種角β和型孔直徑D作為試驗(yàn)因素進(jìn)行充種性能試驗(yàn),,獲得64組單粒率和空穴率的試驗(yàn)結(jié)果,。選取55組結(jié)果作為訓(xùn)練樣本,采用Levenberg-Marquardt訓(xùn)練方法對建立的網(wǎng)絡(luò)進(jìn)行訓(xùn)練,,并選取剩余的9組結(jié)果對訓(xùn)練好的網(wǎng)絡(luò)進(jìn)行仿真預(yù)測,。其中,,n、d,、β和D為網(wǎng)絡(luò)的輸入層,,η1和η2為網(wǎng)絡(luò)的輸出層,網(wǎng)絡(luò)結(jié)構(gòu)為含有單隱層的4-15-2型3層網(wǎng)絡(luò),。預(yù)測結(jié)果表明:預(yù)測值與試驗(yàn)值有較好的一致性,,利用改進(jìn)BP神經(jīng)網(wǎng)絡(luò)對排種器充種性能進(jìn)行預(yù)測是可行的,可為排種器的優(yōu)化設(shè)計(jì)及工作參數(shù)的選擇提供依據(jù),,從而減少試驗(yàn)時(shí)間和成本,。

    Abstract:

    Performance during the seed-filling process directly impacted the seed quality of the metering device. The improved BP neural network prediction model was a metering device that filled at a single-grain rate η1 and the miss rates η2 was established using the Matlab neural network toolbox. The speed n, seed equivalent diameter d, seed-filling angle β and type hole diameter D were selected as the test factors, the test was carried out on 64 groups to determine the single-particle and miss rate. 55 groups were selected from the test as training samples. The Levenberg-Marquardt training method was used to train the establishment of a network. The remaining 9 groups were selected to simulate and predict the trained and improved BP neural network. n, d, β and D were set as the network’s input layers, η1 and η2 were set as the network’s output layers, the network structure was the 4-15-2 type three-layer network containing a single hidden layer. Predicted results showed that predicted values and experimental values were almost same, the predicted performance of seed-filling with the improved BP neural network method was feasible, the method can be used to optimize metering device design and provide a basis for the selection of working parameters, in addition to reducing test time and cost. 

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王沖,宋建農(nóng),王繼承,劉彩玲,李永磊,董向前.基于改進(jìn)BP神經(jīng)網(wǎng)絡(luò)的排種器充種性能預(yù)測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2010,41(Z1):64-67. Performance of Seed-filling Process Based on Improved BP Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(Z1):64-67.

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