共查询到20条相似文献,搜索用时 15 毫秒
1.
In this paper, the technique of image noise cancellation is presented by employing cellular neural networks (CNN) and linear matrix inequality (LMI). The main objective is to obtain the templates of CNN by using a corrupted image and a corresponding desired image. A criterion for the uniqueness and global asymptotic stability of the equilibrium point of CNN is presented based on the Lyapunov stability theorem (i.e., the feedback template “A” of CNN is solved at this step), and the input template “B” of CNN is designed to achieve desirable output by using the property of saturation nonlinearity of CNN. It is shown that the problem of image noise cancellation can be characterized in terms of LMIs. The simulation results indicate that the proposed method is useful for practical application. 相似文献
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Hopfield neural networks on scale-free networks display the power law relation between the stability of patterns and the number of patterns.The stability is measured by the overlap between the output state and the stored pattern which is presented to a neural network.In simulations the overlap declines to a constant by a power law decay.Here we provide the explanation for the power law behavior through the signal-to-noise ratio analysis.We show that on sparse networks storing a plenty of patterns the stability of stored patterns can be approached by a power law function with the exponent-0.5.There is a difference between analytic and simulation results that the analytic results of overlap decay to 0.The difference exists because the signal and noise term of nodes diverge from the mean-field approach in the sparse finite size networks. 相似文献
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Global synchronization of Chua's chaotic delay network by using linear matrix inequality 总被引:3,自引:0,他引:3 下载免费PDF全文
Global synchronization of Chua‘s chaotic dynamical networks with coupling delays is investigated in this paper.Unlike other approaches, where only local results were obtained, the network is found to be not linearized in this paper.Insteat, the global synchronization is obtained by using the linear matrix inequality theory. Moreover, some quite simple linear-state-error feedback controllers for global synchronization are derived, which can be easily constructed based on the minimum eigenvalue of the coupling matrix. A simulation of Chua‘s chaotic network with global coupling delays in nodes is finally given, which is used to verify the theoretical results of the proposed global synchron izationscheme. 相似文献
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针对统计量算法盲检测多进制振幅键控(MPSK)信号的缺陷, 提出了一种幅值相位型连续多值复数Hopfield神经网络算法, 构造了适用于MPSK信号的幅相型离散多电平激活函数,并分别在异步和同步更新模式下证明了该神经网的稳定性.当该神经网的权矩阵借助接收数据补投影算子构成时, 该幅相型离散Hopfield神经网络可有效地实现MPSK信号盲检测. 仿真试验表明:该算法所需接收数据较短,可到达全局真解点,并且适用于含公零点信道. 相似文献
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Recognition ability of the fully connected Hopfield neural network under a persistent stimulus field
We investigate the pattern recognition ability of the fully connected Hopfield model of a neural network under the influence of a persistent stimulus field. The model considers a biased training with a stronger contribution to the synaptic connections coming from a particular stimulated pattern. Within a mean-field approach, we computed the recognition order parameter and the full phase diagram as a function of the stimulus field strength h, the network charge α and a thermal-like noise T. The stimulus field improves the network capacity in recognizing the stimulated pattern while weakening the first-order character of the transition to the non-recognition phase. We further present simulation results for the zero temperature case. A finite-size scaling analysis provides estimates of the transition point which are very close to the mean-field prediction. 相似文献
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The Bayesian neural network approach has been employed to improve the nuclear magnetic moment predictions of odd-A nuclei. The Schmidt magnetic moment obtained from the extreme single-particle shell model makes large root-mean-square (rms) deviations from data, i.e., 0.949 \begin{document}$ \mu_\mathrm{N} $\end{document} ![]()
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and 1.272 \begin{document}$ \mu_\mathrm{N} $\end{document} ![]()
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for odd-neutron nuclei and odd-proton nuclei, respectively. By including the dependence of the nuclear spin and Schmidt magnetic moment, the machine-learning approach precisely describes the magnetic moments of odd-A nuclei with rms deviations of 0.036 \begin{document}$ \mu_\mathrm{N} $\end{document} ![]()
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for odd-neutron nuclei and 0.061 \begin{document}$ \mu_\mathrm{N} $\end{document} ![]()
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for odd-proton nuclei. Furthermore, the evolution of magnetic moments along isotopic chains, including the staggering and sudden jump trend, which are difficult to describe using nuclear models, have been well reproduced by the Bayesian neural network (BNN) approach. The magnetic moments of doubly closed-shell \begin{document}$ \pm1 $\end{document} ![]()
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nuclei, for example, isoscalar and isovector magnetic moments, have been well studied and compared with the corresponding non-relativistic and relativistic calculations. 相似文献
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FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient 下载免费PDF全文
A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and dynamically analyzed,and implemented on FPGA.Then,a new pseudo-random number generator(PRNG)based on MHNN is proposed.The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of PRNG.The experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming language.The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high performance.Finally,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T). 相似文献
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This paper studies delay-dependent asymptotical stability
problems for the neural system with time-varying delay. By dividing the
whole interval into multiple segments such that each segment has a
different Lyapunov matrix, some improved delay-dependent stability
conditions are derived by employing an integral equality technique. A
numerical example is given to demonstrate the effectiveness and
less conservativeness of the proposed methods. 相似文献
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Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays 下载免费PDF全文
In this paper, a synchronization scheme for a class of chaotic
neural networks with time-varying delays is presented. This class of
chaotic neural networks covers several well-known neural networks,
such as Hopfield neural networks, cellular neural networks, and
bidirectional associative memory networks. The obtained criteria are
expressed in terms of linear matrix inequalities, thus they can be
efficiently verified. A comparison between our results and the
previous results shows that our results are less restrictive. 相似文献
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Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays 下载免费PDF全文
Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov–Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method. 相似文献
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由压电驱动器驱动的快速反射镜(FSM)广泛应用于各种精密稳定跟踪系统,FSM的控制精度决定了系统的跟踪精度。但压电驱动器具有严重的迟滞非线性干扰,针对这一缺点,应用自适应径向基RBF神经网络对迟滞干扰进行非线性逼近,并在此基础上结合滑模控制和反演法,设计了自适应反演滑模(ABSM)控制器。仿真实验表明,相对于滑模控制器,ABSM控制器的最大跟踪误差和均方根误差为分别降低了57.26%和52.53%,提高了FSM的控制精度。 相似文献
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New results on global exponential stability of competitive neural networks with different time scales and time-varying delays 下载免费PDF全文
This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria. 相似文献
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人工神经网络是一种强大的非线性数据分析算法,其中的感知器神经网络第一次被用于处理HL-2A装置上汤姆逊散射系统的电子温度数据。采用输入层、隐藏层和输出层等三层神经网络结构,输入层为标定数据或测量数据,隐藏层使用sigmoid函数作为传递函数,输出层为电子温度值。从数据处理结果可以看出,该计算方法与传统的χ2最小值方法计算的结果吻合,能够得到可靠的电子温度数据。而且由于计算温度时采用矩阵计算,计算速度比使用χ2最小值法提高20倍以上,为将来利用汤姆逊散射测量的电子温度数据实现等离子体剖面实时反馈控制提供了可能。 相似文献
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针对受参数不确定和外扰影响的混沌Lorenz系统,提出一种基于径向基函数(RBF)神经网 络的滑模控制方法.基于被控系统在不稳定平衡点处状态误差的可控规范形,设计滑模切换 面并将其作为神经网络的唯一输入.单入单出形式的RBF控制器隐层只需7个径向基函数,网 络的权值则依滑模趋近条件在线确定.仿真表明该控制器对系统参数突变和外部干扰具有鲁棒性,同时抑制了抖振.
关键词:
混沌控制
滑模
径向基函数神经网络
Lorenz系统 相似文献
19.
Spatiotemporal chaos synchronization of an uncertain network based on sliding mode control 下载免费PDF全文
The sliding mode control method is used to study spatiotemporal chaos synchronization of an uncertain network.The method is extended from synchronization between two chaotic systems to the synchronization of complex network composed of N spatiotemporal chaotic systems.The sliding surface of the network and the control input are designed.Furthermore,the effectiveness of the method is analysed based on the stability theory.The Burgers equation with spatiotemporal chaos behavior is taken as an example to simulate the experiment.It is found that the synchronization performance of the network is very stable. 相似文献
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A convolutional neural network approach to calibrating the rotation axis for X‐ray computed tomography 下载免费PDF全文
Xiaogang Yang Francesco De Carlo Charudatta Phatak Dogˇa Gürsoy 《Journal of synchrotron radiation》2017,24(2):469-475
This paper presents an algorithm to calibrate the center‐of‐rotation for X‐ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non‐linearity, etc. An open‐source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided. 相似文献