【摘要】：人工神經(jīng)網(wǎng)絡(luò)由大量處理單元互聯(lián)組成的非線性、自適應(yīng)信息處理系統(tǒng),在模式識別、圖像處理、非線性優(yōu)化等方面得到了大量的應(yīng)用。為了描述系統(tǒng)狀態(tài)的瞬時變化現(xiàn)象,近幾年,人們提出了脈沖神經(jīng)網(wǎng)絡(luò)并進行了大量的理論研究。目前,大多數(shù)對脈沖系統(tǒng)的理論研究主要集中于固定時間的脈沖系統(tǒng)。然而,在實際系統(tǒng)中,脈沖發(fā)生的時刻幾乎是無法預(yù)知的,或者至少是時間相關(guān)的。但由于理論分析的復(fù)雜性,人們對脈沖發(fā)生時刻未預(yù)先給定的脈沖系統(tǒng)的研究還很薄弱。為此,本文研究脈沖時刻不能預(yù)先確定的脈沖神經(jīng)網(wǎng)絡(luò)的定性理論,但為了簡化分析,我們假定脈沖發(fā)生的時刻局限于一個時間區(qū)間內(nèi),即每次脈沖的準(zhǔn)確觸發(fā)時刻不確定,但脈沖總發(fā)生在確定的時間區(qū)間內(nèi)。我們稱這個時間區(qū)間為脈沖時間窗口。本文建立了帶有脈沖時間窗口的脈沖神經(jīng)網(wǎng)絡(luò)模型,并對這些脈沖神經(jīng)網(wǎng)絡(luò)模型的穩(wěn)定性進行分析,得到了一系列確保系統(tǒng)漸近穩(wěn)定性的充分條件。本論文的主要內(nèi)容及貢獻如下:1.推廣固定時間脈沖線性系統(tǒng),建立了帶有脈沖時間窗口的線性脈沖系統(tǒng)模型,并對其穩(wěn)定性問題進行研究,得到了確保系統(tǒng)漸近穩(wěn)定的充分條件。2.將脈沖時間窗口概念引入時滯神經(jīng)網(wǎng)絡(luò)模型,研究了帶有脈沖時間窗口的時滯神經(jīng)網(wǎng)絡(luò)的指數(shù)穩(wěn)定性問題,給出了指數(shù)收斂率與脈沖時間窗口參數(shù)之間的約束關(guān)系,并通過數(shù)值模擬對理論結(jié)果的有效性進行了驗證。3.將脈沖時間窗口概念引入切換神經(jīng)網(wǎng)絡(luò),建立了一類更具一般性的混雜脈沖切換神經(jīng)網(wǎng)絡(luò)模型,通過理論分析得到了該模型指數(shù)穩(wěn)定的充分條件,并通過數(shù)值模擬驗證了理論分析的有效性。
[Abstract]:Artificial neural network (Ann) is a nonlinear adaptive information processing system which is composed of a large number of processing units. It has been widely used in pattern recognition image processing nonlinear optimization and so on. In order to describe the transient state of the system, in recent years, impulse neural networks have been proposed and a large number of theoretical studies have been carried out. At present, most of the theoretical researches on impulsive systems are mainly focused on fixed time impulsive systems. In a real system, however, the time at which the pulse occurs is almost unpredictable, or at least time-dependent. However, due to the complexity of theoretical analysis, the study of impulsive systems which have not been given a given pulse time is still very weak. In this paper, we study the qualitative theory of impulsive neural networks, which can not be determined in advance, but in order to simplify the analysis, we assume that the time of pulse occurrence is limited to a time interval, that is, the exact trigger time of each pulse is uncertain. But the pulse always occurs within a given time interval. We call this time interval a pulse time window. In this paper, the impulsive neural network models with impulsive time windows are established. The stability of these impulsive neural network models is analyzed, and a series of sufficient conditions to ensure the asymptotic stability of the system are obtained. The main contents and contributions of this thesis are as follows: 1. A linear impulsive system model with impulsive time window is established by extending the fixed time impulsive linear system. The stability of the system is studied and the sufficient condition of asymptotic stability of the system is obtained. The concept of impulsive time window is introduced into the time-delay neural network model, and the exponential stability of time-delay neural network with impulsive time window is studied. The constraint relationship between exponential convergence rate and parameters of impulsive time window is given. The validity of the theoretical results is verified by numerical simulation. By introducing the concept of impulsive time window into switching neural networks, a more general hybrid impulsive switching neural network model is established. The sufficient conditions for exponential stability of the model are obtained by theoretical analysis. The validity of the theoretical analysis is verified by numerical simulation.
【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】：TP183

【參考文獻】

相關(guān)期刊論文 前9條

1 陳珊珊;樓旭陽;;二次規(guī)劃問題的新型時滯投影神經(jīng)網(wǎng)絡(luò)的穩(wěn)定性分析[J];網(wǎng)絡(luò)安全技術(shù)與應(yīng)用;2012年09期

2 孫臻;王曙光;王峗玉;劉偉慶;;高層隔震結(jié)構(gòu)非平穩(wěn)隨機地震響應(yīng)與動力可靠度分析[J];建筑結(jié)構(gòu)學(xué)報;2011年12期

3 陸會明;孫敬松;鄧慧;;電站典型大滯后過程自適應(yīng)預(yù)估控制器設(shè)計及應(yīng)用[J];南京航空航天大學(xué)學(xué)報;2006年S1期

4 胡赤兵;滕舟波;張繼有;;基于純滯后補償技術(shù)的加彈機溫控系統(tǒng)[J];紡織學(xué)報;2006年07期

5 張寶琳;唐功友;鄭師;孫亮;;含正弦擾動的離散時滯系統(tǒng)的次優(yōu)減振控制[J];控制與決策;2006年01期

6 曾銘濤,桂衛(wèi)華,唐朝暉;一類變時滯中立型系統(tǒng)的全局指數(shù)穩(wěn)定性研究[J];計算機工程與科學(xué);2005年10期

7 張先明,吳敏,何勇;中立型線性時滯系統(tǒng)的時滯相關(guān)穩(wěn)定性[J];自動化學(xué)報;2004年04期

8 王芳,唐功友;具有小時滯的線性系統(tǒng)次優(yōu)控制的無滯后轉(zhuǎn)換法[J];青島海洋大學(xué)學(xué)報(自然科學(xué)版);2001年02期

9 廖曉昕;細胞神經(jīng)網(wǎng)絡(luò)的數(shù)學(xué)理論(Ⅰ)[J];中國科學(xué)(A輯 數(shù)學(xué) 物理學(xué) 天文學(xué) 技術(shù)科學(xué));1994年09期

相關(guān)碩士學(xué)位論文 前1條

1 韓軼;大跨懸索橋地震響應(yīng)控制研究[D];哈爾濱工業(yè)大學(xué);2011年

，

本文編號：2188384