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基于遺傳模糊神經(jīng)網(wǎng)絡(luò)的植物病斑區(qū)域圖像分割模型
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Image Segmentation Model of Plant Lesion Based on Genetic Algorithm and Fuzzy Neural Network
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    摘要:

    針對(duì)植物病斑區(qū)域圖像邊界的模糊性和不確定性因素,利用模糊邏輯的推理規(guī)則和神經(jīng)網(wǎng)絡(luò)的自適應(yīng)性,,提出全規(guī)則的自適應(yīng)模糊神經(jīng)網(wǎng)絡(luò)模型作為植物病葉圖像像素歸屬的決策系統(tǒng),,并利用遺傳算法對(duì)系統(tǒng)的可調(diào)整參數(shù)初始值進(jìn)行全局優(yōu)化,,提高了網(wǎng)絡(luò)訓(xùn)練速度,,避免了傳統(tǒng)BP算法的局部最小值,。通過對(duì)馬鈴薯早疫病病斑圖像分割的實(shí)驗(yàn)表明,,該模型速度快且穩(wěn)定,,精度高且魯棒性好,簡(jiǎn)單易于實(shí)現(xiàn),。

    Abstract:

    Aiming at the ambiguity and uncertainty of lesion field image border, using inference rule of fuzzy logic and self-adaptive of neural network, the self-adaptive and fuzzy neural network model was proposed to be the decisionsystem for extracting the diseased spots, and the initial values of adjusting parameters were optimized by using genetic algorithm which enhanced the speed of network training, overcame the local minimum of traditional gradient descent method. The experimental result showed that model had many advantages including accuracy, convergence, stability, robustness, and was easy to implement when implied in extracting the diseased spots of potato early blight.

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關(guān)海鷗,許少華,譚峰.基于遺傳模糊神經(jīng)網(wǎng)絡(luò)的植物病斑區(qū)域圖像分割模型[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2010,41(11):163-167. Image Segmentation Model of Plant Lesion Based on Genetic Algorithm and Fuzzy Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(11):163-167.

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