共查询到20条相似文献,搜索用时 156 毫秒
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八面体变几何桁架机构综合的神经网络超混沌牛顿迭代法研究 总被引:1,自引:0,他引:1
神经网络是高度复杂的非线性动力系统,存在着混沌现象.通过消除暂态混沌神经元的模拟退火策略,产生了一种可以永久保持混沌搜索的混沌神经元.研究了由4个该混沌神经元连接的单向循环混沌神经网络拓扑结构和混沌神经网络中存在超混沌现象.应用神经网络超混沌系统产生牛顿迭代法的初始点,首次提出了基于神经网络起混沌的牛顿迭代法求解非线性方程组的新方法.八面体变几何桁架机构综合实例表明了该方法的正确性与有效性. 相似文献
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一种不确定对象的自适应智能PID控制系统 总被引:4,自引:1,他引:3
针对不确定非线性、时滞对象,提出了一种自适应智能PID控制系统.将模糊神经网络和PID神经网络相结合,构成一种智能型PID控制器;控制器参数采用混沌策略与粒子群算法结合的混沌粒子群离线优化和误差反传算法在线调整相结合的方法获得;通过引入最小二乘支持向量机用作辩识器,使控制系统能处理具有未知特性的不确定对象的控制问题.仿真结果表明:通过辩识器的良好非线性映射能力和控制器及其优化算法的有效结合,系统响应速度快、平稳、超调小,且具有一定的鲁棒性,验证了该系统的可行性和有效性. 相似文献
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针对线性时间序列方法无法有效预测云工作流活动的运行时间的问题,提出一种基于混沌时间序列的云工作流活动运行时间预测模型.该模型利用相空间重构理论和径向基函数神经网络实现对非线性时间序列的预测.相空间重构理论能够有效刻画云工作流活动的运行时间因受系统性能、网络状况等多种因素影响而呈现的非线性特征;径向基函数神经网络能够有效预测混沌时间序列.模拟实验分别考虑了计算密集型的科学工作流和实例密集型的商务工作流的情况.实验结果表明,无论长周期活动还是短周期活动,混沌时间序列模型明显优于其他有代表性的活动运行时间预测方法. 相似文献
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针对不确定、时变和非线性机器人系统的实时性要求,提出了采用滑模变结构和RBF神经网络相结合来构造控制器.用带有符号函数的滑模变结构控制器来产生一个控制输入信号,同时利用具有快速学习能力的RBF神经网络来学习外界的不确定性,增强系统的自适应能力,使之达到更佳的控制效果,并在文中证明了系统的稳定性.最后给出了对两连杆机器人的仿真,验证了控制效果. 相似文献
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混沌数字水印及其性能分析 总被引:3,自引:0,他引:3
提出一种在小波变换域嵌入混沌数字水印的新算法.混沌数字水印由Logistic映射产生.由于混沌序列对初值高度敏感,因此可利用的序列数量多,安全性能高.检测水印时将混沌水印序列与水印图像直接进行相关运算,不需要原始图像.文中对相关系数的统计特性进行了详细的理论分析.通过对水印图像进行各种退化处理的实验表明,提出的数字水印算法具有较好的鲁棒性. 相似文献
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基于LVQ神经网络的混沌时间序列分类识别 总被引:3,自引:1,他引:2
学习向量量化 (L VQ)是一种自适应数据分类方法 ,文中研究了利用这种神经网络对 Jeffcott转子碰摩模型的非线性混沌时间序列进行分类识别 ,得到了满意的效果。分析结果表明 ,该方法可以实现对这类混沌信号和其它响应信号数据的聚类 ,对非线性信号分类识别提供了一种较为直接的处理方法 相似文献
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Gou-Jen Wang Bor-Shin Lin Kang J. Chang 《The International Journal of Advanced Manufacturing Technology》2007,32(1-2):42-54
Process control is one of the key methods to improve manufacturing quality. This research proposes a neural network based
run-to-run process control scheme that is adaptive to the time-varying environment. Two multilayer feedforward neural networks
are implemented to conduct the process control and system identification duties. The controller neural network equips the
control system with more capability in handling complicated nonlinear processes. With the system information provided by this
neural network, batch polishing time (T) an additional control variable, can be implemented along with the commonly used down force (p) and relative speed between the plashing pad and the plashed wafer (v).
Computer simulations and experiments on copper chemical mechanical polishing processes illustrate that in drafting suppression
and environmental changing adaptation that the proposed neural network based run-to-run controller (NNRTRC) performs better
than the double exponentially weighted moving average (d-EWMA) approach. It is also suggested that the proposed approach can
be further implemented as both an end-point detector and a pad-conditioning sensor. 相似文献
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In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems. Second, an adaptive radial basis function neural network estimator-based continuous second order sliding mode control algorithm (CSOSMC-ANNE) is adopted. In CSOSMC-ANNE control methodology, a radial basis function neural network with adaptive parameters is exploited to approximate the unknown system parameters and improve performance against perturbations. Also, the discontinuous switching control of SOSMC is supplanted with a smooth continuous control action to completely eliminate the chattering phenomenon. The convergence and global stability of the closed-loop system are proved using Lyapunov stability method. Numerical computer simulations, with dynamical model of the nonlinear inverted pendulum system, are presented to demonstrate the effectiveness and advantages of the presented control scheme. 相似文献
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In this paper, a novel fault detection and identification (FDI) scheme for a class of nonlinear systems is presented. First of all, an augment system is constructed by making the unknown system faults as an extended system state. Then based on the ESO theory, a novel fault diagnosis filter is constructed to diagnose the nonlinear system faults. An extension to a class of nonlinear uncertain systems is then made. An outstanding feature of this scheme is that it can simultaneously detect and identify the shape and magnitude of the system faults in real time without training the network compared with the neural network-based FDI schemes. Finally, simulation examples are given to illustrate the feasibility and effectiveness of the proposed approach. 相似文献
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应用复合正交神经网络来实现过程的自适应逆控制方法,和通用模型控制器策略相结合,提出了一种基于神经网络的通用模型自适应控制方法,将非线性过程模型应用逆系统的方法可以在控制算法中直接嵌入过程模型,从而保证通用模型控制策略的可实现性.另一方面,在自适应逆控制中采用复合正交神经网络具有算法简单、学习收敛速度快等优点,可以克服常用的BP和RBF神经网络一些缺点.基于神经网络的通用模型自适应控制方法中的参考轨迹是一条典型的二阶曲线,该控制器参数具有明显的物理意义,参数整定方便.仿真验证了该控制策略的有效性. 相似文献
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Adaptive NN output-feedback stabilization for a class of stochastic nonlinear strict-feedback systems 总被引:2,自引:0,他引:2
In this paper, the adaptive neural network output-feedback stabilization problem is investigated for a class of stochastic nonlinear strict-feedback systems. The nonlinear terms, which only depend on the system output, are assumed to be completely unknown, and only an NN is employed to compensate for all unknown upper bounding functions, so that the designed controller is more simple than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the closed-loop system can be proved to be asymptotically stable in probability. The simulation results demonstrate the effectiveness of the proposed control scheme. 相似文献
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研究了设备故障趋势的预测方法,介绍了非线性自回归模型,提出将BP神经网络与非线性自回归模型相结合,针对实验室JZQ250型齿轮箱的测试系统建立了基于振动信号的神经网络预测模型。采用MATLAB软件中自带的神经网络工具箱,利用模块化的编程思想,编程实现了神经网络预测模型.并利用实验室数据的峭度指标进行了实验。首先给出网络的输入及对应的目标输出,然后经过训练获得网络的权值和阈值,最终构建齿轮箱故障趋势的预测神经网络,用来预测齿轮箱的故障趋势。结果表明,该模型能够有效地短期预测齿轮箱的典型故障,可以用于齿轮箱的故障诊断。 相似文献
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针对双足机器人的混合动力学系统辨识问题,从系统渐进稳定性角度分析,推导出连续与离散混合系统的可辨识条件,提出了一种基于混沌粒子群优化的径向基函数神经网络与动态模糊神经网络的联合辨识方法。利用混沌粒子群优化的径向基函数神经网络辨识双腿的连续摆动阶段,利用动态模糊神经网络辨识离散的足地碰撞阶段;依据两阶段同一变量的耦合、转换关系,实现了对双足机器人整体混合系统的准确辨识。仿真实验结果表明,该方法辨识和预测结果具有较高的准确度。 相似文献