本文關(guān)鍵詞： WSN節(jié)點和網(wǎng)絡(luò)故障 聯(lián)合神經(jīng)網(wǎng)絡(luò) 故障征兆信號 徑向基Elman神經(jīng)網(wǎng)絡(luò) 雙參數(shù)實數(shù)編碼　出處：《電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,物聯(lián)網(wǎng)被視為繼計算機(jī)、互聯(lián)網(wǎng)和移動通信之后的又一項信息產(chǎn)業(yè)的革命性技術(shù)而得到廣泛重視。作為物聯(lián)網(wǎng)的一種實現(xiàn)形式之一,無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSN)顯示出良好的應(yīng)用前景。實際應(yīng)用過程中,WSN往往工作在復(fù)雜、惡劣的環(huán)境中,很容易因受到干擾、損壞而出現(xiàn)故障,從而嚴(yán)重影響其工作效率和質(zhì)量。同時,因其工作環(huán)境的限制及自身的技術(shù)特點,這些故障很難人為親自加以排除,診斷技術(shù)也有別與傳統(tǒng)網(wǎng)絡(luò)。因此,節(jié)點及網(wǎng)絡(luò)故障診斷與容錯是無線傳感器網(wǎng)絡(luò)技術(shù)研究的重要內(nèi)容之一。根據(jù)無線傳感器網(wǎng)絡(luò)的結(jié)構(gòu)、功能特性,結(jié)合以有故障診斷方法,針對無線傳感器網(wǎng)絡(luò)中可能出現(xiàn)的節(jié)點故障和網(wǎng)絡(luò)故障,本文合理提取出用于節(jié)點故障診斷和網(wǎng)絡(luò)故障診斷的多個故障征兆信號,并在此基礎(chǔ)上提出了一種由兩級功能不同神經(jīng)網(wǎng)絡(luò)組成聯(lián)合神經(jīng)網(wǎng)絡(luò)的無線傳感器網(wǎng)絡(luò)節(jié)點和網(wǎng)絡(luò)故障診斷方案。第一級神經(jīng)網(wǎng)絡(luò)作為預(yù)測器,用于預(yù)測節(jié)點傳感器的輸出,檢測WSN節(jié)點傳感器的故障,以此產(chǎn)生傳感器單元故障征兆信號。在該預(yù)測器實現(xiàn)上,對Elman神經(jīng)網(wǎng)絡(luò)進(jìn)行了改進(jìn),提出了一種徑向基Elman神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),并對其訓(xùn)練算法進(jìn)行了推導(dǎo)。然后建立了傳感器故障仿真模型,對基于徑向基Elman神經(jīng)網(wǎng)絡(luò)傳感器單元故障檢測進(jìn)行了仿真驗證。第二級神經(jīng)網(wǎng)絡(luò)作為分類器,用于對所有故障征兆信號進(jìn)行模式分類,以實現(xiàn)對WSN可能的故障的類型進(jìn)行判別,該神經(jīng)網(wǎng)絡(luò)采用RBF神經(jīng)網(wǎng)絡(luò)實現(xiàn)。為了提升該網(wǎng)絡(luò)的訓(xùn)練效率,對量子遺傳算法進(jìn)行了深入研究,提出了一種量子遺傳算法的雙參數(shù)實數(shù)編碼方式,并結(jié)合現(xiàn)有的混合遞階遺傳算法,提出了一種混合遞階量子遺傳算法,用于RBF分類器的學(xué)習(xí)。該故障診斷方案的驗證通過計算機(jī)仿真與實物實驗相結(jié)合的方式進(jìn)行,先在計算機(jī)上對故障診斷方法中涉及到的各項技術(shù)進(jìn)行逐一仿真驗證,而后整體在無線傳感器網(wǎng)絡(luò)實物上進(jìn)行測試驗證。仿真、實驗表明,該無線傳感器網(wǎng)絡(luò)故障診斷方法,能同時對無線傳感器網(wǎng)絡(luò)的節(jié)點級與網(wǎng)絡(luò)級故障進(jìn)行診斷,并具有較高的故障檢測率,滿足實際應(yīng)用的基本要求。本文在理論上的探索,將對神經(jīng)網(wǎng)絡(luò)和進(jìn)化算法的進(jìn)一步發(fā)展起到一定的參考作用。
[Abstract]:In recent years, the Internet of things has received extensive attention as a revolutionary technology in the information industry after computers, the Internet of things and mobile communications. Wireless Sensor Networks (WSNs) show good application prospects. In practical applications, WSNs often work in complex and harsh environments, and are prone to malfunction due to interference and damage. Therefore, due to the limitations of its working environment and its own technical characteristics, these faults are difficult to be personally eliminated and the diagnostic techniques are different from those of traditional networks. Node and network fault diagnosis and fault tolerance is one of the important contents of wireless sensor network technology. According to the structure and function of wireless sensor network, combining with the method of fault diagnosis, Aiming at the possible node faults and network failures in wireless sensor networks, this paper reasonably extracts multiple fault symptom signals for node fault diagnosis and network fault diagnosis. On the basis of this, a scheme of node and network fault diagnosis of wireless sensor network composed of two-level neural networks with different functions is proposed. The first stage neural network is used as predictor to predict the output of node sensor. The fault of WSN node sensor is detected to produce the signal of fault symptom of sensor unit. In the realization of the predictor, the Elman neural network is improved, and a radial basis function (Elman) neural network structure is proposed. Then the sensor fault simulation model is established, and the fault detection of sensor unit based on radial basis function (Elman) neural network is simulated and verified. The second stage neural network is used as classifier. In order to improve the training efficiency of WSN, the neural network is implemented by RBF neural network, which is used to classify the patterns of all fault symptom signals in order to distinguish the possible fault types of WSN. In this paper, quantum genetic algorithm (QGA) is studied in detail. A quantum genetic algorithm (QGA) with two parameters is proposed, and a hybrid hierarchical quantum genetic algorithm (HQGA) is proposed in combination with the existing hybrid hierarchical genetic algorithm (HGA). It is used in the learning of RBF classifier. The method of fault diagnosis is verified by computer simulation and physical experiment. The simulation results show that the method can diagnose both node level and network level fault of wireless sensor network. It has high fault detection rate and meets the basic requirements of practical application. The theoretical exploration in this paper will play a certain reference role in the further development of neural network and evolutionary algorithm.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212.9;TN929.5;TP183
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本文編號：1526881