Chaotic behavior in noninteger-order cellular neural networks

In this paper, a simple system showing chaotic behavior is introduced. It is based on the well-known concept of cellular neural networks (CNNs), which have already given good results in generating complex dynamics. The peculiarity of the CNN model consists in the fact that it replaces the traditional first-order cell with a noninteger-order one. The introduction of the fractional cell, with a suitable choice of the coupling parameters, leads to the onset of chaos in a simple two-cell system. A theoretical approach, based on the harmonic balance theory, has been used to investigate the existence of chaos. A circuit realization of the proposed fractional two-cell chaotic CNN is reported and the corresponding strange attractor is also shown.     

Quantum enhanced convolutional neural networks for NISQ computers

正The interplay between quantum physics and machine learning may lead to unprecedented perspectives for both fields [1]. On the one hand, ideas and techniques from machine learning, or more broadly artificial intelligence, can be exploited to tackle challenging problems in the quantum domain.     

量子过程神经网络模型算法及应用

为提高过程神经网络的逼近和泛化能力, 从研究过程神经元信息处理的量子计算实现机理入手, 提出基于量子旋转门及多位受控非门的物理意义构造量子过程神经元的新思想. 将离散化后的过程式输入信息作为受控非门的控制位, 经过量子旋转门作用后控制目标量子位的状态, 以目标量子位处于状态|1>概率幅作为量子过程神经元的输出. 以量子过程神经元为隐层, 普通神经元为输出层, 可构成量子过程神经网络. 基于量子计算机理推导了该模型的学习算法. 将该模型用于太阳黑子数年均值预测, 应用结果表明, 所提方法与普通过程神经网络相比, 预测精度有所提高, 对于复杂预测问题具有一定理论意义和实用价值.     

Global exponential convergence analysis of delayed cellular neural networks

Some sufficient criteria have been established to ensure the global exponential stability of delayed cellular neural networks by using an approach based on delay differential inequality. Compared with the method of Lyapunov functionals as in most previous studies, our method is simpler and more effective for a stability analysis of delayed system. Some previously established results in the literature are shown to be special cases of the present result.     

A recurrent perceptron learning algorithm for cellular neural networks

A supervised learning algorithm for obtaining the template coefficients in completely stable Cellular Neural Networks (CNNs) is analysed in the paper. The considered algorithm resembles the well-known perceptron learning algorithm and hence called as Recurrent Perceptron Learning Algorithm (RPLA) when applied to a dynamical network. The RPLA learns pointwise defined algebraic mappings from initial-state and input spaces into steady-state output space; despite learning whole trajectories through desired equilibrium points. The RPLA has been used for training CNNs to perform some image processing tasks and found to be successful in binary image processing. The edge detection templates found by RPLA have performances comparable to those of Canny's edge detector for binary images.     

量子细胞神经网络的超混沌特性研究

研究了由量子点细胞自动机构成的量子细胞神经网络的非线性动力学特性.以量子点细胞的极化率和量子相位作为状态变量，对3个细胞耦合的量子细胞神经网络进行了理论分析和计 算机仿真研究.结果表明，该网络系统呈现复杂的混沌动力学行为，混沌振荡产生非常容易. 由数值计算得到的两个最大正Lyapunov指数证实了该系统具有超混沌特性. 关键词： 量子点细胞自动机 极化率 量子细胞神经网络 超混沌     

Quantum neural networks: Current status and prospects for development

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.     

Chaotic phenomena in Josephson circuits coupled quantum cellular neural networks

In this paper the nonlinear dynamical behaviour of a quantum cellular neural network (QCNN) by coupling Josephson circuits was investigated and it was shown that the QCNN using only two of them can cause the onset of chaotic oscillation. The theoretical analysis and simulation for the two Josephson-circuits-coupled QCNN have been done by using the amplitude and phase as state variables. The complex chaotic behaviours can be observed and then proved by calculating Lyapunov exponents. The study provides valuable information about QCNNs for future application in high-parallel signal processing and novel chaotic generators.     

Asymptotical stability of multi-delayed cellular neural networks with impulsive effects

In this work, the stability issues of the equilibrium points of multi-delayed cellular neural networks with impulsive effects are investigated. Based on the method of linear matrix inequality (LMI) and parameterized first-order model transformation, several new delay-dependent and delay-independent asymptotical stability conditions are derived by the stability theory of Lyapunov–Krasovskii. A numerical example is given to illustrate the effectiveness of our results.     

Periodic solutions and exponential stability in delayed cellular neural networks

Some simple sufficient conditions are given ensuring global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov functionals and some analysis techniques. These conditions are easy to check in terms of system parameters and have important leading significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, two examples are given to illustrate the theory.     

Global exponential stability for nonautonomous cellular neural networks with delays

In this Letter, by utilizing Lyapunov functional method and Halanay inequalities, we analyze global exponential stability of nonautonomous cellular neural networks with delay. Several new sufficient conditions ensuring global exponential stability of the network are obtained. The results given here extend and improve the earlier publications. An example is given to demonstrate the effectiveness of the obtained results.     

Chaotification for a class of cellular neural networks with distributed delays

In this Letter, the chaotification for a class of cellular neural networks with distributed delays is studied. On the basis of the largest Lyapunov exponent, the sensitivity to the initial conditions is studied for the distributed delays with kernel being weak and strong. Some theoretical results about the chaotification for the neural network with distributed time delays are derived. Finally, two numerical simulations are presented to illustrate the effectiveness of the theoretical results.     

Global asymptotic stability of fuzzy cellular neural networks with time-varying delays

In this Letter fuzzy cellular neural networks with time-varying delays are studied. Sufficient conditions for the existence, uniqueness and global asymptotic stability of equilibrium point are established by using the theory of topological degree and applying the properties of nonsingular M-matrix. The activation functions are not required to be differentiable, bounded or monotone nondecreasing. The results of this Letter are new and they complement previously known results.     

Synchronization of delayed fuzzy cellular neural networks based on adaptive control

This Letter studies synchronization of delayed fuzzy cellular neural networks with all the parameters unknown. To enhance the coupled strength dynamically and be more suitable for the reality, we add fuzzy theory to the traditional cellular neural networks. By the Lyapunov-Lasall principle of functional differential equations, some new stability criteria are obtained via adaptive control. To the best of our knowledge, there has few work studying fuzzy cellular neural networks. Moreover, the approaches developed here extend the ideas and techniques derived in recent literatures. In the end, an example and its simulation were given to illustrate the simpleness and effectiveness of our main results.     

The characteristics of nonlinear chaotic dynamics in quantum cellular neural networks

With the polarization of quantum-dot cell and quantum phase serving as state variables, this paper does both theoretical analysis and simulation for the complex nonlinear dynamical behaviour of a three-cell-coupled Quantum Cellular Neural Network （QCNN）, including equilibrium points, bifurcation and chaotic behaviour. Different phenomena, such as quasi-periodic, chaotic and hyper-chaotic states as well as bifurcations are revealed. The system＇s bifurcation and chaotic behaviour under the influence of the different coupling parameters are analysed. And it finds that the unbalanced cells coupled QCNN is easy to cause chaotic oscillation and the system response enters into chaotic state from quasi-periodic state by quasi-period bifurcation; however, the balanced cells coupled QCNN also can be chaotic when coupling parameters is in some region. Additionally, both the unbalanced and balanced cells coupled QCNNs can possess hyper-chaotic behaviour. It provides valuable information about QCNNs for future application in high-parallel signal processing and novel ultra-small chaotic generators.     

Stationary oscillation for chaotic shunting inhibitory cellular neural networks with impulses

In this paper, we study stationary oscillation for general shunting inhibitory cellular neural networks with impulses which are complex nonlinear neural networks. In a recent paper [Z. J. Gui and W. G. Ge, Chaos 16, 033116 (2006)], the authors claimed that they obtained a criterion of existence, uniqueness, and global exponential stability of periodic solution (i.e., stationary oscillation) for shunting inhibitory cellular neural networks with impulses. We point out in this paper that the main result of their paper is incorrect, and presents a sufficient condition of ensuring existence, uniqueness, and global stability of periodic solution for general shunting inhibitory cellular neural networks with impulses. The result is derived by using a new method which is different from those of previous literature. An illustrative example is given to demonstrate the effectiveness.     

具有时滞的一阶细胞神经网络动态行为研究

研究了具有时滞的一阶细胞神经网络的复杂动力学行为-证明了Hopf分叉的存在性，指出了 若选择适当的参数，则该网络中可以产生混沌-计算机仿真实验表明所得结论的正确性-由于 该网络的构成较简单，因此实际中可以作为混沌发生器加以应用- 关键词： 细胞神经网络 时滞 Hopf分叉 混沌