本文選題：遺忘混和時變時滯隨機神經(jīng)網(wǎng)絡(luò)系統(tǒng) + 全局均方穩(wěn)定��； 參考：《遼寧大學》2017年碩士論文

【摘要】：人工神經(jīng)網(wǎng)絡(luò)具有自組織、自學習、快速找尋最優(yōu)化解的能力,并在模式識別、人工智能、自動控制、通信和物流等領(lǐng)域有廣泛應用.本文主要研究混和時變時滯隨機神經(jīng)網(wǎng)絡(luò)系統(tǒng)的耗散控制問題.主要內(nèi)容如下:首先,介紹神經(jīng)網(wǎng)絡(luò)的歷史發(fā)展及系統(tǒng)概況,給出本文的結(jié)構(gòu)與安排;其次,研究遺忘隨機神經(jīng)網(wǎng)絡(luò)的全局均方穩(wěn)定的問題,運用隨機穩(wěn)定性理論和線性矩陣不等式等方法,得到系統(tǒng)全局均方穩(wěn)定的充分條件,并設(shè)計狀態(tài)估計器使誤差系統(tǒng)漸近穩(wěn)定,通過兩個數(shù)值算例驗證結(jié)論的可行性;再次,分別對離散和連續(xù)混合時變時滯隨機神經(jīng)網(wǎng)絡(luò)系統(tǒng)進行γ耗散、指數(shù)耗散和指數(shù)無源研究,運用LMI方法,得到系統(tǒng)γ耗散、指數(shù)耗散和指數(shù)無源的充分條件,通過數(shù)值算例驗證結(jié)論的可行性;最后對本文進行總結(jié).
[Abstract]:Artificial neural network has the ability of self-organization, self-learning and fast searching for optimal solution, and it has been widely used in the fields of pattern recognition, artificial intelligence, automatic control, communication and logistics. In this paper, the dissipative control problem of stochastic neural network systems with mixed time-varying delays is studied. The main contents are as follows: firstly, the history and system of neural networks are introduced, and the structure and arrangement of this paper are given. Secondly, the problem of global mean square stability of forgotten stochastic neural networks is studied. By using stochastic stability theory and linear matrix inequality (LMI), the sufficient conditions for global mean square stability of the system are obtained, and a state estimator is designed to make the error system asymptotically stable. The feasibility of the conclusion is verified by two numerical examples. The 緯 dissipation, exponential dissipation and exponential passivity of discrete and continuous hybrid time-varying delay stochastic neural networks are studied, respectively. By using LMI method, sufficient conditions of 緯 dissipation, exponential dissipation and exponential passivity are obtained. The feasibility of the conclusion is verified by numerical examples. Finally, this paper is summarized.
【學位授予單位】：遼寧大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O175

【參考文獻】

相關(guān)博士學位論文 前1條

1 楊麗;廣義系統(tǒng)耗散控制問題的研究[D];東北大學;2006年

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本文編號：1949209