Prescribed-Time Synchronization of Coupled Memristive Neural Networks with Heterogeneous Impulsive Effects

Neural Processing Letters - This paper is concerned with the prescribed-time synchronization of coupled memristive neural networks (MNNs). The impulsive effects with heterogeneous impulsive...     

Fixed-Time Synchronization of Complex-Valued Memristor-Based Neural Networks with Impulsive Effects

Neural Processing Letters - In this paper, the fixed-time synchronization of complex-valued memristor-based neural networks with impulsive effects is investigated. We first separate these...     

Finite-Time Synchronization of Memristive Neural Networks with Proportional Delay

The problem of finite-time synchronization for memristive neural networks (MNNs) with proportional delay is considered. Since proportional delay is unbounded and different from infinite-time distributed delay, the classical finite-time analytical techniques are not applicable anymore. First, a discontinuous state feedback controller is designed such that the delayed MNNs achieve drive-response synchronization in a finite settling time. By using Filippov solution and Lyapunov functional method, sufficient conditions are derived. It is shown that, though the proportional delay is unbounded, complete synchronization can still be realized and the settling time can be explicitly estimated. Second, a special adaptive controller is designed for the finite-time problem in order to reduce the control gains. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.     

Exponential Synchronization of Stochastic Memristive Neural Networks with Time-Varying Delays

Neural Processing Letters - This paper pays attention to the synchronization control methodology for stochastic memristive system. On the framework of Lyapunov functional, stability theory and...     

Synchronization of Switched Coupled Neural Networks with Distributed Impulsive Effects: An Impulsive Strength Dependent Approach

Neural Processing Letters - The main focus of this paper is to investigate synchronization of delayed impulsive switched coupled neural networks, in which both synchronizing and desynchronizing...     

Quantized Control for Synchronization of Delayed Fractional-Order Memristive Neural Networks

Neural Processing Letters - This research addresses the synchronization of delayed fractional-order memristive neural networks (DFMNNs) via quantized control. The motivations are twofold: (1) the...     

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.

     

The Optimization of Control Parameters: Finite-time and Fixed-time Synchronization of Inertial Memristive Neural Networks with Proportional Delays and Switching Jumps Mismatch

This thesis’s object is inertial memristive neural networks (IMNNs) with proportional delays and switching jumps mismatch. Different from the traditional bounded delay, the proportional delay will be infinite as t → ∞. The finite-time synchronization (FN-TS) and fixed-time synchronization (FX-TS) can be realized with the devised controllers for the drive-response systems (D-RSs). Along with the Lyapunov function and some inequalities, the synchronization criteria of D-RSs are given. This paper presents an optimization model with minimum control energy and dynamic error as objective functions, aiming to obtain more accurate and optimized controller parameters. An intelligent algorithm: particle swarm optimization with stochastic inertia weight (SIWPSO) algorithm is introduced to solve the optimization model. Meanwhile, an integrated algorithm for selecting optimal control parameters is presented as well. In this method, the optimal control parameters and the setting time of synchronization can be obtained directly. At last, some simulations are presented to verify the theorems and the optimization model.

     

Complex Projection Synchronization of Fractional-Order Complex-Valued Memristive Neural Networks with Multiple Delays

Neural Processing Letters - In this paper, the complex projection synchronization problem of fractional-order complex-valued memristive neural networks is investigated, in which the projection...     

Fixed-time Synchronization of Complex-valued Memristive BAM Neural Network and Applications in Image Encryption and Decryption

International Journal of Control, Automation and Systems - This paper focuses on the dynamical characteristics of complex-valued memristor-based BAM neural network (CVMBAMNN) with leakage...     

Anti-periodic Synchronization of Quaternion-valued Generalized Cellular Neural Networks with Time-varying Delays and Impulsive Effects

International Journal of Control, Automation and Systems - In this paper, a class of quaternion-valued generalized cellular neural networks (QVGCNNs) with time-varying delays and impulsive effects...     

Exponential Stability and Sampled-Data Synchronization of Delayed Complex-Valued Memristive Neural Networks

Neural Processing Letters - This paper is devoted to the global exponential stability (GES) and synchronization control of delayed complex-valued memristive neural networks (CVMNNs). The criterion...     

Delay-Dependent Robust Exponential Stability of Impulsive Markovian Jumping Reaction-Diffusion Cohen-Grossberg Neural Networks

This paper is devoted to investigating delay-dependent robust exponential stability for a class of Markovian jump impulsive stochastic reaction-diffusion Cohen-Grossberg neural networks (IRDCGNNs) with mixed time delays and uncertainties. The jumping parameters, determined by a continuous-time, discrete-state Markov chain, are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing a Lyapunov–Krasovskii functional, and using poincarè inequality and the mathematical induction method, several novel sufficient criteria ensuring the delay-dependent exponential stability of IRDCGNNs with Markovian jumping parameters are established. Our results include reaction-diffusion effects. Finally, a Numerical example is provided to show the efficiency of the proposed results.     

Impulsive Stabilization and Synchronization of Fractional-Order Complex-Valued Neural Networks

This paper focuses on the impulsive stabilization of fractional-order complex-valued neural networks. Based on impulsive control and some fractional-order     

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．     

Global Exponential Synchronization of Memristive Competitive Neural Networks with Time-Varying Delay via Nonlinear Control

Automatically describing contents of an image using natural language has drawn much attention because it not only integrates computer vision and natural language processing but also has practical applications. Using an end-to-end approach, we propose a bidirectional semantic attention-based guiding of long short-term memory (Bag-LSTM) model for image captioning. The proposed model consciously refines image features from previously generated text. By fine-tuning the parameters of convolution neural networks, Bag-LSTM obtains more text-related image features via feedback propagation than other models. As opposed to existing guidance-LSTM methods which directly add image features into each unit of an LSTM block, our fine-tuned model dynamically leverages more text-conditional image features, acquired by the semantic attention mechanism, as guidance information. Moreover, we exploit bidirectional gLSTM as the caption generator, which is capable of learning long term relations between visual features and semantic information by making use of both historical and future contextual information. In addition, variations of the Bag-LSTM model are proposed in an effort to sufficiently describe high-level visual-language interactions. Experiments on the Flickr8k and MSCOCO benchmark datasets demonstrate the effectiveness of the model, as compared with the baseline algorithms, such as it is 51.2% higher than BRNN on CIDEr metric.     

Pinning Synchronization of Coupled Memristive Recurrent Neural Networks with Mixed Time-Varying Delays and Perturbations

Neural Processing Letters - In this paper, the pinning synchronization of coupled memristive recurrent neural networks (MNNs) with mixed time-varying delays and perturbations is investigated....     

Impulsive Synchronization of Stochastic Neural Networks via Controlling Partial States

In this paper the perceptron neural networks are applied to approximate the solution of fractional optimal control problems. The necessary (and also sufficient in most cases) optimality conditions are stated in a form of fractional two-point boundary value problem. Then this problem is converted to a Volterra integral equation. By using perceptron neural network’s ability in approximating a nonlinear function, first we propose approximating functions to estimate control, state and co-state functions which they satisfy the initial or boundary conditions. The approximating functions contain neural network with unknown weights. Using an optimization approach, the weights are adjusted such that the approximating functions satisfy the optimality conditions of fractional optimal control problem. Numerical results illustrate the advantages of the method.     

具有信道时延的混沌神经网络脉冲同步

由于控制脉冲只是在特定的时间序列产生,使得同步系统中所需的驱动系统信息和能量减少,从而给混沌系统同步设计带来巨大方便。但是,由于实际电路中器件的切换速度有限且内在通讯需要时间。在通信网络中将不可避免地产生时延。因此,现有的一些同步方法将无法实现。针对这种情况,提出了一种基于脉冲控制的混沌神经网络同步策略。在该策略中考虑了信道时延带来的影响,并设计了控制器实现两个混沌神经网络的同步。计算机仿真结果验证了该方法的可行性和有效性。