Exponential Stabilization of Stochastic Memristive Recurrent Neural Networks Under Periodically Intermittent State Feedback Control

In this paper, the exponential stabilization problem is investigated for a class of memristive time‐varying delayed neural networks with stochastic disturbance via periodically intermittent state feedback control. First, a periodically intermittent state feedback control rule is designed for the exponential stabilization of stochastic memristive time‐varying delayed neural networks. Then, by adopting appropriate Lyapunov‐Krasovskii functionals in light of the Lyapunov stability theory, some novel stabilization criteria are obtained to guarantee exponential stabilization of stochastic memristive time‐varying delayed neural networks via periodically intermittent state feedback control. Compared with existing results on stabilization of stochastic memristive time‐varying delayed neural networks, the obtained stabilization criteria in this paper are not difficult to verify. Finally, an illustrative example is given to illustrate the validity of the obtained results.     

Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

This paper considers the global exponential synchronization problem of two memristive chaotic recurrent neural networks with time‐varying delays using periodically alternate output feedback control. First, the periodically alternate output feedback control rule is designed for the global exponential synchronization of two memristive chaotic recurrent neural networks. Then, according to the Lyapunov stability theory, we construct an appropriate Lyapunov‐Krasovskii functional to derive several new sufficient conditions guaranteeing exponential synchronization of two memristive chaotic recurrent neural networks under periodically alternate output feedback control. Compared with existing results on synchronization conditions on the basis of linear matrix inequalities of memristive chaotic recurrent neural networks, the derived results complement, extend earlier related results, and are also easy to validate in this paper. An illustrative example is provided to illustrate the effectiveness of the synchronization criteria.     

Exponential stabilisation of memristive neural networks under intermittent output feedback control

In this paper, the exponential stabilisation problem is studied for a general class of memristive time-varying delayed neural networks under periodically intermittent output feedback control. First, the periodically intermittent output feedback control rule is designed for the exponential stabilisation of the memristive time-varying delayed neural networks. Then, we derive stabilisation criteria so that the memristive time-varying delayed neural networks are exponentially stable. By the mathematical induction method and constructing suitable Lyapunov–Krasovskii functionals, some easy-to-check criteria are obtained to ensure the exponential stabilisation of memristive time-varying delayed neural networks. Finally, two numerical simulation examples are given to illustrate the validity of the obtained results.     

一类时滞混沌忆阻器神经网络的延迟反同步控制

研究了一类时滞混沌忆阻器神经网络的延迟反同步控制问题．通过构造李亚普诺夫函数及采用微分包含理论和Halanay不等式的研究方法,设计了一个线性反馈控制器,并恰当选择控制器增益实现了一类混沌忆阻器神经网络驱动系统与响应系统之间的延迟反同步,所设计的控制器简单并易于实现．最后,仿真例子验证了所设计的控制器的有效性．     

汽包水位的模糊神经网络预测模型研究

针对汽包水位的时滞、非线性特性，用模糊神经网络建立了它的d步预测模型。详细介绍了建模的机理和模型结构，并用C语言编程进行了实验，验证了汽包水位模糊神经网络建模的可行性。     

具有时滞的不确定性系统神经网络模糊自学习控制

本文对具有时滞的不确定性控制对象提出了一种神经网络时滞补偿模糊自学习控制方法．模糊控制器采用误差、误差变化及误差加速度的加权和的解析描述形式，利用人工神经网络直接对过程建模，实现对时滞补偿预报以及对模糊加权因子的自学习优化调整．将上述方法用于焊接熔池动态过程控制试验，结果表明本文提出的自学习神经网络时滞补偿模糊控制方案有效．     

Exponential dissipativity analysis of discrete‐time switched memristive neural networks with actuator saturation via quasi‐time‐dependent control

The dissipativity of discrete‐time switched memristive neural networks with actuator saturation is considered in this paper. By constructing a quasi‐time‐dependent Lyapunov function, sufficient conditions are obtained to guarantee the exponential stability and exponential dissipativity for the closed‐loop system with mode‐dependent average dwell time switching. Furthermore, the exponential H_∞ performance of discrete‐time switched memristive neural networks is also analyzed, while the quasi‐time‐dependent controller and observer gains of the desired exponential dissipative and H_∞ performance can be calculated from linear matrix inequalities. Finally, the effectiveness of theoretical results is illustrated through the numerical examples.     

Exponential stability of impulsive high-order Hopfield-type neural networks with delays and reaction–diffusion

The problem of global exponential stability analysis of Impulsive high-order Hopfield-type neural networks with time-varying delays and reaction–diffusion terms has been investigated in this paper. Using the Lyapunov function method and M-matrix theory, we establish the global exponential stability of the neural networks with its estimated exponential convergence rate. As an illustration, a numerical example is given using the results.     

一种基于RBF网络提取模糊规则的算法实现

径向基函数网络和模糊推理系统在一些柔和的情况下具有等价的功能,因此可以利用神经网络的学习算法来调节模糊系统的参数,学习后的模糊系统具有自学习和自组织性,但是削弱了模糊系统的可解释性。将模糊逻辑推理与神经网络控制技术相结合,分析了一种改进的径向基函数(RBF)神经网络结构,这种模糊神经网络结构能够有效地表达模糊系统可解释性这一突出特点,也使模糊系统具有了较好的自学习和自组织能力、通过VC 实现了基于这种RBF网络结构提取模糊规则的算法,并进行了仿真实验,仿真结果表明该算法是比较有效的。     

基于模糊神经网络的机械故障诊断方法的研究

本文针对机械传动系统典型零部件的故障，应用振动谱分析方法，给出故障诊断的模糊规则，并采用模糊神经网络实现诊断推理，文中举例说明了该诊断方法。     

多变量系统的模糊神经网络控制模型及其应用

本文综合模糊控制系统与人工神经网络的优点，提出了一种多变量系统的模糊神经网络控制模型并给出了其建模方法，该方法适合于多变量系统的建模及其模糊控制器的设计。笔者以此方法建立了渣贫化电炉生产过程的模糊神经网络控制模型并开发出相应的决策支持系统，该系统自１９９２年６月投入生产现场使用以来，一直稳定可靠地正常运行，取得了令人满意的效果和显著的经济和社会效益。     

The Intermittent Control Synchronization of Complex-Valued Memristive Recurrent Neural Networks with Time-Delays

In this paper, the intermittent control synchronization of complex-valued memristive recurrent neural networks with time-delays is investigated. As a generalization on the real-valued memristive recurrent neural networks, complex-valued memristive recurrent neural networks own more complicated properties. In complex-valued domain, bounded and analytic complex-valued activation functions do not exist. Some assumptions about activation functions in real-valued domain cannot be applied directly to complex-valued fields. By appropriate transformation, complex-valued memristive recurrent neural networks can be divided into real parts and imaginary parts, which can avoid discussing the bounded and analytic. In the framework of differential inclusion theory and Lyapunov method, sufficient criteria of intermittent control synchronization are established. Finally, a simulation is given to verify the validity and feasibility of the sufficient conditions.

     

基于小波模糊神经网络的实时交通流预测

实时交通流预测是智能运输系统研究的重要内容之一.本文将小波分析的相关知识与模糊神经网络相结合,给出了基于小波模糊神经网络的交通流预测模型,采用小波函数作为模糊隶属度函数,用神经网络来实现模糊推理,完成对下一个周期性交通流的估计.同时,用遗传算法来优化整个网络,实测数据验证这种方法预测精度高,收敛过程平稳,适应性强.     

Global Lagrange Stability for Takagi-Sugeno Fuzzy Cohen-Grossberg BAM Neural Networks with Time-varying Delays

This paper concerns the globally exponential stability in Lagrange sense for Takagi-Sugeno (T-S) fuzzy Cohen-Grossberg BAM neural networks with time-varying delays. Based on the Lyapunov functional method and inequality techniques, two different types of activation functions which include both Lipschitz function and general activation functions are analyzed. Several sufficient conditions in linear matrix inequality form are derived to guarantee the Lagrange exponential stability of Cohen-Grossberg BAM neural networks with time-varying delays which are represented by T-S fuzzy models. Finally, simulation results demonstrate the effectiveness of the theoretical results.     

Fixed-time Synchronization of Memristive Cohen-Grossberg Neural Networks with Impulsive Effects

This paper investigates the fixed-time synchronization of memristive Cohen-Grossberg neural networks with impulsive effects. Through a nonlinear transformation and Fillipov discontinuous theory, we obtain an equivalent system from the original memristive Cohen-Grossberg neural networks. By constructing a discontinuous Lyapunov function and utilizing comparison principle, a sufficient condition is achieved to guarantee the fixed-time synchronization of drive-response system with impulsive effects. Moreover, for the purpose of reducing the cost of control, an adaptive control strategy is considered. Finally, corresponding numerical simulations are carried out to show the effectiveness of the analytic results.     

时滞Hopfield神经网络的随机稳定性分析

T-S模型提供了一种通过模糊集和模糊推理将复杂的非线性系统表示为线性子模型的方法。研究了时滞Hopfield神经网络的随机稳定性（SFVDHNNs）。首先描述了SFVDHNNs模型,然后用Lyapunov方法研究了SFVDHNNs全局均方指数稳定性,通过可以被一些标准的数值分析方法求解的线性矩阵不等式（LMIs）得出了稳定性标准。     

基于模糊神经网络的机器人滑模自适应控制

滑模控制（SMC）响应快,对系统参数和外部扰动呈不变性,可保证系统的渐近稳定性,但其缺点是控制存在很强的抖动;而模糊神经网络（FNN）具有模糊系统和神经网络共同的特点。将滑模控制和模糊神经网络控制有机结合,利用简单得到的学习信号对模糊神经网络进行在线学习,通过平滑切换函数实现直接自适应控制策略。对两连杆机械手的仿真研究表明,在存在模型误差和外部扰动的情况下,该方案既能达到高精度快速跟踪的目的,又能有效减小滑模控制的抖动问题。     

一种改进的RBF网络结构及其参数的设计方法

针对神经网络在学习之后,模糊系统的原始结构被改变,或削弱了规则可解释性这一模糊系统突出特点的问题,给出了一种提取模糊If-then规则的径向基函数(RBF)神经网络结构。该神经网络结构具有能够同时清晰表达模糊控制系统输入空间划分和模糊规则可解释性的特点,克服了以往用神经网络提取模糊规则不能直观体现模糊语言规则可解释性的不足,并详细地讨论了此网络结构参数的设计方法。     

Exponential synchronization of coupled memristive neural networks with time delays

In this paper, the model of coupled memristive neural networks with time delays is established, and sufficient conditions are obtained that guarantee the exponential synchronization for such system. Memristive network exhibits state-dependent switching behaviors due to the physical properties of memristor. It is demonstrated that the synchronization performance is largely dependent on the coupling pattern and strength among memristive neural networks. Moreover, the information exchange graph of the underlying network topology need not be undirected or strongly connected. Finally, numerical simulations are given to verify the usefulness and effectiveness of our results.     

具有学习功能的多值模糊神经网络控制系统

对于具有非线性、大时滞、不确定性等特性的难以用精确数学模型描述的多变量复杂系统,靠传统控制理论难以获得理想的控制效果。基于模糊神经网络控制技术不依赖于被控对象精确的数学模型,且能根据被控对象参数的变化自适应调节控制规则和隶属函数参数的特性,进行了采用模糊神经网络控制器实现其控制的应用研究。采用典型的前向型模糊神经网络模型,给出了具有学习功能的多值模糊神经网络控制系统的一种设计方法。仿真实验证明,该系统能够获得较理想的控制效果。