本文選題：混沌神經(jīng)網(wǎng)絡(luò) + 變時滯��； 參考：《西南大學(xué)》2017年碩士論文

【摘要】：如今是一個網(wǎng)絡(luò)信息發(fā)達(dá)的時代,資訊隨處可見,信息到處可達(dá),然而卻隱藏著信息安全的問題。尤其是在公安、軍事和國防等相關(guān)領(lǐng)域中的保密通信,對國家安全、利益以及人民的生命財(cái)產(chǎn)安全有著直接的關(guān)系。同時,互聯(lián)網(wǎng)上的信息傳輸和日常的電話聯(lián)絡(luò)的安全保密性能也受到廣大研究者和用戶的關(guān)注。如何確保信息在傳輸過程中的安全性,最重要的途徑即是在利用相關(guān)加密技術(shù)對信息進(jìn)行加密傳輸。那么,設(shè)計(jì)一個保密性能強(qiáng)、安全系數(shù)高的加密信號至關(guān)重要�；煦缧盘栴愃圃肼曇话�,具有隱蔽性強(qiáng)、運(yùn)動軌跡復(fù)雜、難以破解和預(yù)測等特點(diǎn),其非常適合在保密通信中充當(dāng)加密信號的角色。隨著神經(jīng)網(wǎng)絡(luò)和混沌學(xué)的不斷發(fā)展以及相互滲透,學(xué)者們發(fā)現(xiàn),神經(jīng)網(wǎng)絡(luò)在某種條件下可以產(chǎn)生混沌現(xiàn)象。混沌神經(jīng)網(wǎng)絡(luò)不僅結(jié)構(gòu)簡單易于用硬件電路實(shí)現(xiàn),而且具有復(fù)雜的混沌動力學(xué)行為,能夠產(chǎn)生高度復(fù)雜且具有無窮維的混沌信號,可以滿足保密通信中對加密信號有較高的要求。所以,具有混沌現(xiàn)象的神經(jīng)網(wǎng)絡(luò)非常適合做加密信號。其次,神經(jīng)網(wǎng)絡(luò)中神經(jīng)元之間的信息傳輸通常都會產(chǎn)生時滯。時滯會誘發(fā)神經(jīng)網(wǎng)絡(luò)產(chǎn)生更加復(fù)雜的混沌時間序列,使得加密信息能力的安全系數(shù)更高。此外,在接受端要把加密之前的信息提取出來,就需要用到混沌同步技術(shù)。由于時滯的引入,加大了同步控制器設(shè)計(jì)難度。在實(shí)際工程中,系統(tǒng)參數(shù)不確定也會給同步控制性能帶來很大的影響。本文的主要研究工作如下:第一,針對具有參數(shù)不確定性的變時滯混沌神經(jīng)網(wǎng)絡(luò),設(shè)計(jì)魯棒控制器以保證兩個結(jié)構(gòu)相同、初始值不同的混沌神經(jīng)網(wǎng)絡(luò)同步。其中,參數(shù)不確定性是時變且范數(shù)有界。利用采樣控制技術(shù),考慮了兩個隨機(jī)發(fā)生且概率已知的采樣周期。建立了含有隨機(jī)變量的同步誤差狀態(tài)方程,并構(gòu)造新的李雅譜諾夫泛函,推導(dǎo)出魯棒同步的充分條件。通過MATLAB的LIM工具箱,求解得到合適的控制器反饋增益矩陣以保證兩個相同結(jié)構(gòu)的具有參數(shù)不確定性的混沌時滯神經(jīng)網(wǎng)絡(luò)的全局均方魯棒同步。此外,相比于周期采樣,利用隨機(jī)采樣可以得到更大的采樣周期。第二,針對同時具有離散時滯和分布時滯的混沌神經(jīng)網(wǎng)絡(luò),同樣使用隨機(jī)采樣的控制和輸入延遲的方法,設(shè)計(jì)同步控制器。在基于兩個采樣周期的基礎(chǔ)上,推廣到多個采樣周期,把兩個系統(tǒng)的同步問題轉(zhuǎn)化為含有隨機(jī)變量的同步誤差狀態(tài)方程的穩(wěn)定性問題,并且重新構(gòu)建新的李雅譜諾夫泛函,利用不等式技術(shù)和自由權(quán)矩陣的方法,得到全局均方同步的充分條件。所得到的結(jié)果,比同等模型的周期采樣更有優(yōu)越性。
[Abstract]:Now is a network information developed era, information can be seen everywhere, information can be reached everywhere, but hidden the problem of information security. Especially in the public security, military, defense and other related areas of confidential communications, has a direct relationship with national security, interests and the safety of people's lives and property. At the same time, the information transmission on the Internet and the security performance of daily telephone contact are also concerned by researchers and users. The most important way to ensure the security of information transmission is to use the related encryption technology to encrypt the information. So, it is very important to design an encrypted signal with strong security and high safety factor. Chaotic signal is similar to noise and has the characteristics of strong concealment complex motion trajectory difficult to decipher and prediction etc. It is very suitable to play the role of encrypted signal in secure communication. With the development and mutual penetration of neural network and chaos, scholars find that the neural network can produce chaos under certain conditions. Chaotic neural network not only has simple structure and easy to be realized by hardware circuit, but also has complex chaotic dynamic behavior, which can produce highly complex and infinite dimensional chaotic signals. It can satisfy the requirement of encrypted signal in secure communication. Therefore, the neural network with chaotic phenomena is very suitable for making encrypted signals. Secondly, information transmission between neurons in neural networks usually produces time delay. The delay will induce the neural network to produce more complicated chaotic time series, which makes the security factor of encryption information higher. In addition, chaotic synchronization is needed to extract the information before encryption at the receiving end. Because of the introduction of time delay, it is more difficult to design synchronous controller. In practical engineering, the uncertainty of system parameters will also have a great impact on the performance of synchronous control. The main work of this paper is as follows: firstly, a robust controller is designed for chaotic neural networks with variable delay with parameter uncertainty to ensure synchronization of two chaotic neural networks with the same structure and different initial values. The parameter uncertainty is time-varying and norm bounded. Two random sampling periods with known probability are considered by sampling control technique. The state equation of synchronization error with random variables is established and a new Lyapunov Functionals are constructed. The sufficient conditions for robust synchronization are derived. By using MATLAB's LIM toolbox, an appropriate controller feedback gain matrix is obtained to ensure the global mean square robust synchronization of two chaotic time-delay neural networks with the same structure. In addition, compared with periodic sampling, the random sampling can be used to obtain a larger sampling period. Secondly, for chaotic neural networks with both discrete and distributed delays, a synchronization controller is designed using the method of random sampling control and input delay. On the basis of two sampling periods, the synchronization problem of two systems is transformed into the stability of synchronization error equation of state with random variables, and a new Lyapunov functional is constructed. A sufficient condition for global mean square synchronization is obtained by using inequality technique and free matrix method. The results obtained are superior to the periodic sampling of the same model.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O415.5;TP183

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