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基于量子遺傳模糊神經(jīng)網(wǎng)絡(luò)的蘋果果實(shí)識(shí)別
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國家自然科學(xué)基金資助項(xiàng)目(31071333)和中央高校基本科研業(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(2013YJ008)


Apple Recognition Based on Fuzzy Neural Network and Quantum Genetic Algorithm
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    摘要:

    針對(duì)田間蘋果采摘機(jī)器人視覺系統(tǒng)中彩色圖像邊界像素的模糊性和不確定性影響蘋果果實(shí)識(shí)別精度和速度問題,,提出了一種將量子遺傳算法的全局搜索能力和模糊推理神經(jīng)網(wǎng)絡(luò)的自適應(yīng)性相結(jié)合的算法來識(shí)別蘋果果實(shí)。利用量子遺傳算法對(duì)模糊神經(jīng)網(wǎng)絡(luò)的可調(diào)整參數(shù)初始值進(jìn)行了全局優(yōu)化,,加快了網(wǎng)絡(luò)學(xué)習(xí)速度,避免了傳統(tǒng)BP誤差反向傳播學(xué)習(xí)算法易陷入局部極小值,、迭代次數(shù)多等弊端,。實(shí)驗(yàn)表明:該識(shí)別模型高速且穩(wěn)定,魯棒性好,,對(duì)于果實(shí)本身顏色不均勻樣本正確識(shí)別率為100%,,對(duì)自然光照引起顏色不均勻樣本正確識(shí)別率為96.86%,對(duì)鄰接圖像正確識(shí)別率為94.29%,,對(duì)重疊圖像正確識(shí)別率為92.31%,。

    Abstract:

    The apple images were hard to be identified at a faster speed and a higher accuracy because of fuzzy and uncertain factors existing in the color image boundary pixels, so in order to overcome the disadvantages above, a model combined quantum genetic algorithm and fuzzy neural network was built up which showed the capability of global search capability and adaptation. In the proposed model, quantum genetic algorithm was used to optimize the initial value of adjustable parameter in fuzzy neural network, which avoided redundant iteration and the incline to fall into the local minimum value of traditional BP algorithm. The experimental results showed that the proposed model achieved accuracy of 100% for the uneven color samples, 96.86% for sunlight influenced samples, 94.29% for the adjacent samples, and 92.31% for the overlapping samples.

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馬曉丹,劉剛,周薇,馮娟.基于量子遺傳模糊神經(jīng)網(wǎng)絡(luò)的蘋果果實(shí)識(shí)別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2013,44(12):227-232,251. Ma Xiaodan, Liu Gang, Zhou Wei, Feng Juan. Apple Recognition Based on Fuzzy Neural Network and Quantum Genetic Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(12):227-232,251.

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  • 在線發(fā)布日期: 2013-12-05
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