【摘要】：針對無線傳感器網(wǎng)絡節(jié)點故障診斷中存在的冗余故障屬性、噪聲數(shù)據(jù)以及數(shù)據(jù)可靠性等問題,提出基于粗糙集-優(yōu)化概率神經(jīng)網(wǎng)絡的無線傳感器網(wǎng)絡節(jié)點故障診斷算法(簡稱RSOPNN)。通過粗糙集從故障樣本屬性集合中求解故障診斷屬性約簡,從而去除冗余故障屬性,降低冗余屬性、噪聲數(shù)據(jù)對故障診斷的影響,節(jié)省能耗。對于多個屬性約簡選擇,以屬性間的相關程度作為度量標準,代替常規(guī)的主觀選擇,從多個約簡中確定最優(yōu)故障診斷屬性約簡,解決主觀選擇的不合理性。以最優(yōu)的故障診斷屬性重構故障樣本,作為優(yōu)化概率神經(jīng)網(wǎng)絡的輸入,建立故障分類模型,從而對故障進行診斷。實驗結果表明,在不同的數(shù)據(jù)可靠性下,RSOPNN方法能夠有效刪減樣本中的冗余屬性和噪聲數(shù)據(jù),保持高效的故障診斷水平,符合無線傳感器網(wǎng)絡的需求。
[Abstract]:Aiming at the problems of redundant fault attributes, noise data and data reliability in node fault diagnosis of wireless sensor networks, A novel Node Fault diagnosis algorithm for Wireless Sensor Networks based on rough set and optimal probabilistic Neural Networks (RSOPNN).) The reduction of fault diagnosis attribute is solved by rough set from fault sample attribute set, so that redundant fault attribute is removed, redundant attribute is reduced, the influence of noise data on fault diagnosis is reduced, and energy consumption is saved. For multiple attribute reduction selection, the correlation degree between attributes is taken as a measure instead of conventional subjective selection, and the optimal fault diagnosis attribute reduction is determined from multiple reduction to solve the irrationality of subjective selection. The optimal fault diagnosis attribute is used to reconstruct the fault sample as the input of the optimized probabilistic neural network and the fault classification model is established to diagnose the fault. The experimental results show that under different data reliability, the RSOPNN method can effectively delete redundant attributes and noise data from the samples and maintain an efficient fault diagnosis level, which meets the needs of wireless sensor networks.
【作者單位】： 西北大學信息科學與技術學院;西北大學現(xiàn)代教育技術中心;
【基金】：國家科技支撐計劃課題(No.2013BAK01B02) 國家自然科學基金(No.61373176) 陜西省重大科技創(chuàng)新專項資金項目(No.2012ZKC05-2)
【分類號】：TP18;TP212.9;TN929.5

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