共查询到20条相似文献,搜索用时 15 毫秒
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This article presents a genetic-algorithm-based prediction model for forecasting traffic demands of next-generation wireless networks that are expected to be chaotic in nature. The model approximates the best-fit mathematical equation that generates a given time series using a genetic algorithm. It estimates future traffic in wireless networks using the most recent traffic data points collected from the actual network. Such estimations will be beneficial for network operators helping to manage and optimise the limited radio resources efficiently and eventually to facilitate cognitive radio applications. The new model is compared with conventional regressions analysis and exponential smoothing models, and it has been found that the genetic algorithm model successfully recovers the underlying mathematical expression describing chaotic time series in less than 200 generations and the predictions achieved are by far better than those of regression and exponential smoothing models. The model also offers benefits for in cognitive communication systems with their intrinsic learning capabilities and distributed access decisions. 相似文献
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A multivariable predictor algorithm is presented which can produce multistep prediction of a stochastic time series. The algorithm is used to predict product quality in an iron-ore sintering process. The application uses a modified low-pass filtering technique involving less time lag than conventional filters. 相似文献
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基于混沌时间序列建模的频谱状态持续时长预测 总被引:1,自引:0,他引:1
为提高频谱利用率,该文利用非线性动力学理论对频谱状态持续时长序列进行建模并预测。以实际采集的频谱数据作为研究对象,采用指向导数法对该时长序列进行非一致延长时间相空间重构,利用基于尺度的Lyapunov指数判定其混沌特性。以基于Davidon-Fletcher-Powell方法的二阶Volterra预测模型 (DFPSOVF)为基础,提出一种基于限域拟牛顿方法的Volterra自适应滤波器系数调整模型,并将该模型应用于具有混沌特性的短时频谱状态持续时长预测,通过自适应剔除对预测贡献小的滤波器系数,降低预测模型的复杂度。实验结果表明该算法在保证预测精度的同时降低运算复杂度。 相似文献
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一种新的混沌扩频序列产生方法 总被引:2,自引:0,他引:2
本文提出了一种新的混沌扩频序列产生方法。该方法基于神经网络的强大学习能力和副近非线性函数能力,应用具有全局最优的BP改进算法通过训练学习建立起具有混沌性态的优化神经网络模型,利用网络权值调整的灵活性来产生混沌扩频序列。计算机仿真结果表明,该模型产生的混沌扩频序列调整更容易,比基于单一混沌映射能产生更多符合扩频通信要求的扩频序列。 相似文献
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对复杂非线性系统的相空间重构理论进行了研究分析,提出了混沌背景中微弱信号检测的回声状态网络方法。针对回声状态网络模型参数选取困难这一问题,采用遗传算法对其模型参数进行优化。将回声状态网络模型参数作为遗传算法的个体,混沌时间序列预测均方根误差的倒数作为适应度函数,通过选择、交叉、变异等操作获得适合数据特点的最优模型参数。根据回声状态网络强大的学习和非线性处理能力,利用得到的回声状态网络模型最优参数建立混沌背景噪声的单步预测模型,将淹没在混沌背景噪声中的微弱瞬态信号和周期信号从预测误差中检测出来。以Lorenz系统和实测的海杂波数据作为混沌背景噪声进行仿真实验,仿真结果表明,本文所提方法在预测精度和训练速度方面均优于支持向量机和神经网络模型,能够有效地检测出混沌背景噪声中的微弱目标信号,且具有较小的预测误差。 相似文献
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Accurate fault prediction can obviously reduce cost and decrease the probability of accidents so as to improve the performance of the system testing and maintenance. Traditional fault prediction methods are always offline that are not suitable for online and real-time processing. For the complicated nonlinear and non-stationary time series, it is hard to achieve exact predicting result with single models such as support vector regression (SVR), artifieial neural network (ANN), and autoregressive moving average (ARMA). Combined with the accurate online support vector regression (AOSVR) algorithm and ARMA model, a new online approach is presented to forecast fault with time series prediction. The fault trend feature can be extracted by the AOSVR with global kernel for general fault modes. Moreover, its prediction residual that represents the local high-frequency components is synchronously revised and compensated by the sliding time window ARMA model. Fault prediction with combined AOSVR and ARMA can be realized better than with the single one. Experiments on Tennessee Eastman process fault data show the new method is practical and effective. 相似文献
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该文以通信系统中常用的典型微波部件——同轴连接器为研究对象,基于混沌理论对获得的同轴连接器的无源互调(PIM)功率时间序列进行分析,验证了使用混沌理论预测无源互调的有效性。首先通过实验系统获得同轴连接器的3阶无源互调功率时间序列,并对得到的实验数据进行相空间重构,确定该时间序列的最佳嵌入维数m和延迟时间τ。然后,结合最佳嵌入维数和延迟时间,分别构建相图和使用小数据量法计算该时间序列的最大Lyapunov指数,从而从定性和定量角度验证了该无源互调功率时间序列具有混沌特性。在此基础上,基于获得的最大Lyapunov指数对该无源互调功率时间序列进行混沌预测,在最大可预测尺度范围内,理论预测值与实验值最大误差为2.61%,表明采用混沌方法预测无源互调功率效果较好。该文提出的使用混沌理论预测通信系统中微波部件无源互调功率的方法,为开展无源互调抑制技术研究,提高通信系统的性能提供了新思路。
相似文献11.
A new efficient method that compensates for unreliable nodes in network reliability computations is presented. This method can be embedded in the modified Dotson algorithm or any algorithm that generates a symbolic reliability expression for networks with perfect nodes. Its cost increases linearly with the number of links, and the effect of unreliable nodes can be directly computed. This method supplants the Aggarwal method and other methods of compensating for unreliable nodes in the calculation of node-pair reliability. When combined with the modified Dotson algorithm, this method provides accurate reliability estimates for networks so large that the Theologou-Carlier algorithm cannot complete its computations in a reasonable amount of time. For such networks, the new method can be embedded in the modified Dotson algorithm to estimate both the node-pair reliability and the error in this estimate even if the algorithm is terminated before completion 相似文献
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Chaos control on universal learning networks 总被引:2,自引:0,他引:2
Hirasawa K. Murata J. Jinglu Hu ChunZhi Jin 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》2000,30(1):95-104
A new chaos control method is proposed which is useful for taking advantage of chaos and avoiding it. The proposed method is based on the following facts: (1) chaotic phenomena can be generated and eliminated by controlling the maximum Lyapunov exponent of the systems, and (2) the maximum Lyapunov exponent can be formulated and calculated by using higher-order derivatives of universal learning networks (ULNs). ULNs consist of a number of interconnected nodes which may have any continuously differentiable nonlinear functions in them and where each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm has been derived for the ULNs in which both first-order derivatives (gradients) and higher-order derivatives are incorporated. In simulations, parameters of ULNs with bounded node outputs were adjusted for the maximum Lyapunov exponent to approach the target value, and it has been shown that a fully-connected ULN with three sigmoidal function nodes is able to generate and eliminate chaotic behaviors by adjusting these parameters 相似文献
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基于神经网络的联合混沌时间序列的预测研究 总被引:3,自引:3,他引:0
提出了利用前馈神经网络预测联合混沌序列,通过引用著名的Henon和Lozi混沌系统作为仿真实验产生联合混沌信号序列。预测结果证明,用改进的BP算法训练的NN可以完全预测联合混沌信号序列。 相似文献
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基于混沌理论与改进回声状态网络的网络流量多步预测 总被引:2,自引:0,他引:2
网络流量预测是网络管理及网络拥塞控制的重要问题,针对该问题提出一种基于混沌理论与改进回声状态网络的网络流量预测方法。首先利用0-1混沌测试法与最大Lyapunov指数法对不同时间尺度下的网络流量样本数据进行分析,确定网络流量在不同时间尺度下都具有混沌特性。将相空间重构技术引入网络流量预测,通过C-C方法确定延迟时间,G-P算法确定嵌入维数。对网络流量时间序列进行相空间重构之后,利用一种改进的回声状态网络进行网络流量的多步预测。提出一种改进的和声搜索优化算法对回声状态网络的相关参数进行优化以提高预测精度。利用网络流量的公共数据集以及实际数据进行了仿真,结果表明,提出的预测方法具有更高的预测精度以及更小的预测误差。 相似文献
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锂离子电池应用时表现出的时变、动态、非线性等特征,以及容量再生现象,导致传统模型对锂离子电池剩余使用寿命(RUL)预测的准确性低,该文将变分模态分解(VMD)和高斯过程回归(GPR)以及动态自适应免疫粒子群(DAIPSO)结合,建立RUL预测模型。首先利用等压降放电时间分析法,提取健康因子,利用VMD对其进行分解处理,挖掘数据内在信息,降低数据复杂度,并针对不同分量,利用不同协方差函数建立GPR预测模型,有效捕获了数据的长期下降趋势和短期再生波动。利用DAIPSO算法优化GPR模型,实现核函数超参数的优化,建立了更准确的退化关系模型,最终实现剩余使用寿命的准确预测,以及不确定性表征。最后利用NASA电池数据进行验证,离线预测结果表明所提方法具有较高预测精度和泛化适应能力。 相似文献
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为了提高非线性卫星钟差预测的精度,降低单一钟差预测模型对钟差预测的风险,提出了一种组合模型的卫星钟差预测算法.该算法首先采用db1小波对卫星钟差序列进行3层多分辨率分解和单支重构,得到一个趋势分量和三个细节分量,然后运用灰色预测模型对重构后的趋势分量和混沌一阶加权局域预测法对重构后的细节分量分别进行预测,最后将各分量预测结果相加后得到总的钟差预测值.以GPS卫星钟差数据做算例分析,在6小时的钟差预测中,算法绝对误差最大值比单一的灰色预测模型误差小1.3ns以上.将该组合预测模型用于非线性卫星钟差预测中,可以提高钟差预测的精度和可靠性. 相似文献
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利用混沌时间序列短期可以预测的特点,构建煤矿瓦斯浓度预测模型,重构煤矿瓦斯浓度相空间,采用改进的C-C算法确定相空间时延t和相空间嵌入维数m。然后在重构相空间中,运用加权一阶局域法构建煤矿瓦斯浓度的预测模型,进行煤矿瓦斯浓度预测。 相似文献