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基于量子神經(jīng)網(wǎng)絡(luò)的馬鈴薯早疫病診斷模型
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國家自然科學(xué)基金資助項目(60473051);黑龍江省農(nóng)墾總局科技攻關(guān)資助項目(HNKXIV—09—04b,、HNK10A—07—02)


Diagnosis Method of Potato Early Blight Based on Quantum Neural Network
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    摘要:

    針對馬鈴薯早疫病智能診斷,，將量子計算的態(tài)疊加方法和神經(jīng)網(wǎng)絡(luò)計算的自適應(yīng)性結(jié)合，提出了將量子神經(jīng)網(wǎng)絡(luò)作為馬鈴薯早疫病診斷模型。該模型隱含層采用多個量子能級的激勵函數(shù)疊加的量子神經(jīng)元,，有效地解決了病害診斷中模糊決策，在給出的學(xué)習(xí)算法的訓(xùn)練過程中自適應(yīng)地確定樣本特征數(shù)據(jù)中的不確定性,。此算法能夠較好地避免傳統(tǒng)神經(jīng)網(wǎng)絡(luò)在訓(xùn)練過程中易出現(xiàn)局部極小值的弊端,，提高了網(wǎng)絡(luò)學(xué)習(xí)速度。仿真結(jié)果表明：量子神經(jīng)網(wǎng)絡(luò)在馬鈴薯早疫病診斷中,，診斷正確率達(dá)到96.5%,。

    Abstract:

    In order to realize the intelligent diagnosis of potato early blight, with the combination of the linear superposition of quantum computing ideas and adaptive neural network computation, a quantum neural network model for diagnosis of potato early blight was built. The model used multiple quantum energy levels of the hidden layer activation function of the linear superposition of quantum neuron model. Fuzzy decision of disease diagnosis was effectively solved. Uncertainty characteristics of sample data was adaptively given in training process to determine. The algorithm overcame the disadvantages of local minimum and increased learning efficiency and training speed. The simulation results showed that the diagnosis accuracy reached to 96.5%. 

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馬曉丹,譚峰,許少華.基于量子神經(jīng)網(wǎng)絡(luò)的馬鈴薯早疫病診斷模型[J].農(nóng)業(yè)機(jī)械學(xué)報,2011,42(6):174-178,183. Ma Xiaodan, Tan Feng, Xu Shaohua. Diagnosis Method of Potato Early Blight Based on Quantum Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(6):174-178,183.

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