共查询到19条相似文献,搜索用时 109 毫秒
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在跳频通信的研究中,针对实现实时跳频跟踪干扰问题,跳频预测干扰是通信电子战中跳频干扰中一项有效的技术.传统的跳频预测干扰的跟踪模型是跳频码序列预测.跳频码序列预测技术在处理实际的跳频通信码序列时实时性差,命中率并不高.为解决上述问题,提出了改进的跟踪模型,跳频同步频率集预测.同时还建立了更客观的新的预测性能评价标准模型.进行仿真的结果表明,与跳频码序列预测相比,跳频同步频率集预测有更高的实时性,使跳频预测提高了跳频跟踪干扰的效率. 相似文献
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混沌作为一种非线性动态系统中的现象正在受到人们的极大重视,混沌理论的发展和不断成熟给跳频码序列的研究带来了新的希望。文中主要介绍混沌跳频序列的产生原理和一种以MCS-51单片机为核心配以其它辅助电路实现的混沌跳频序列产生器。 相似文献
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本文采用混沌序列作为跳时超宽带(TH-UWB)系统和直接序列超宽带(DS-UWB)系统的跳时码和扩频码,针对混沌跳时脉冲位置调制、混沌跳时脉冲幅度调制和混沌直接序列脉冲幅度调制在加性高斯白噪声干扰下的误码率性能进行了分析。并用Matlab对性能进行了仿真,将仿真结果与理论分析结果进行比较,得出一些有益的结论。 相似文献
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跳频序列发生器是跳频通信系统的核心部件,寻求理想的跳频序列是研究跳频通信的重要课题之一.通过对RS码跳频序列相关技术的研究,改进了基于RS码宽间隔非重复跳频序列的优化方法,利用Matlab软件进行了仿真,并绘制出了某个跳频序列的汉明相关性.计算结果表明,该方法克服了对偶频带法产生的宽间隔的跳频序列随机性较差的缺点,提高了整个跳频序列的平均间隔,满足相邻频率值大于某个固定的常数,同时也满足宽间隔非重复跳频序列族的理论界限. 相似文献
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In this paper we have proposed code M-ary frequency shift keying technique based frequency hopping (CMFSK/FH) spread spectrum signaling for multi channel. In this technique, taking one bit from each channel, we have constructed data word (called as symbol) for CMFSK system and corresponding to each state of data word, the transmitter generates a frequency, which is selected and amplified for transmission. The received signal in the receiver is tuned to each transmitted frequency and decoded for separation of channels. Here, we have developed simulation model for bit error rate (BER) performance analysis of CMFSK/FH system and compared the same with analytical results of BER. It is seen that simulation model is matched closely with analytical result. The BER performance for CMFSK/FH is better than that of existing FH system. It is also seen that the BER decreases with increase of frequency hop size. 相似文献
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In this paper, an integrated model based on efficient extreme learning machine (EELM) and differential evolution (DE) is proposed to predict chaotic time series. In the proposed model, a novel learning algorithm called EELM is presented and used to model the chaotic time series. The EELM inherits the basic idea of extreme learning machine (ELM) in training single hidden layer feedforward networks, but replaces the commonly used singular value decomposition with a reduced complete orthogonal decomposition to calculate the output weights, which can achieve a much faster learning speed than ELM. Moreover, in order to obtain a more accurate and more stable prediction performance for chaotic time series prediction, this model abandons the traditional two-stage modeling approach and adopts an integrated parameter selection strategy which employs a modified DE algorithm to optimize the phase space reconstruction parameters of chaotic time series and the model parameter of EELM simultaneously based on a hybrid validation criterion. Experimental results show that the proposed integrated prediction model can not only provide stable prediction performances with high efficiency but also achieve much more accurate prediction results than its counterparts for chaotic time series prediction. 相似文献
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交通流量VNNTF神经网络模型多步预测研究 总被引:1,自引:0,他引:1
研究了VNNTF 神经网络(Volterra neural network trafficflow model,VNNTF) 交通流量混沌时间序列多步预测问题. 通过分析比较交通流量混沌时间序列相空间重构的嵌入维数和Volterra 离散模型之间的关系,给出了确定交通流量Volterra 级数模型截断阶数和截断项数的方法,并在此基础上建立了VNNTF 神经网络交通流量时间序列模型;设计了交通流量Volterra 神经网络的快速学习算法;最后,利用交通流量混沌时间序列对VNNTF 网络模型,Volterra 预测滤波器和BP 网络进行了多步预测实验,比较了多步预测结果的仿真图、绝对误差的柱状图以及归一化后的方均根;实验结果表明VNNTF 神经网络的多步预测性能明显优于Volterra 预测滤波器和BP 神经网络. 相似文献
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In this paper, a novel solving method for speech signal chaotic time series prediction model was proposed. A phase space was reconstructed based on speech signal's chaotic characteristics and the genetic programming (GP) algorithm was introduced for solving the speech chaotic time series prediction models on the phase space with the embedding dimension m and time delay τ. And then, the speech signal's chaotic time series models were built. By standardized processing of these models and optimizing parameters, a speech signal's coding model of chaotic time series with certain generalization ability was obtained. At last, the experimental results showed that the proposed method can get the speech signal chaotic time series prediction models much more effectively, and had a better coding accuracy than linear predictive coding (LPC) algorithms and neural network model. 相似文献
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混沌的特性决定了混沌系统很难长期预测,支持向量机有强大的学习能力,根据相空间重构理论用支持向量机建立预测模型对混沌时间序列进行短期预测。预测输出构建混沌吸引子来定性评价预测模型性能,同时与BP神经网络RBF神经网络构建的预测模型比较,计算预测模型的均方根误差定量地评价模型的性能。仿真结果表明,该方法具有较高的预测精度和泛化能力。 相似文献
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在混沌时间序列研究中,相空间重构和预测模型参数优化是影响预测性能的关键步骤,利用两者之间的相互联系来提高混沌时间序列预测模型的整体性能,提出一种基于遗传算法的混沌时间序列参数同步优化方法。同步优化方法将相空间重构和最小二乘支持向量机参数作为遗传算法的染色体,预测精度作为遗传算法的适应度函数值,通过遗传算法对参数同步优化问题进行求解。通过混沌时间数据对同步优化方法进行了验证性实验。实验结果表明,相对于单独参数优化方法,同步优化方法不仅提高了混沌时间序列的预测精度,同时降低了计算时间的复杂度。 相似文献
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Li-yun Su 《Computers & Mathematics with Applications》2010,59(2):737-744
To improve the prediction accuracy of complex multivariate chaotic time series, a novel scheme formed on the basis of multivariate local polynomial fitting with the optimal kernel function is proposed. According to Takens Theorem, a chaotic time series is reconstructed into vector data, multivariate local polynomial regression is used to fit the predicted complex chaotic system, then the regression model parameters with the least squares method based on embedding dimensions are estimated,and the prediction value is calculated. To evaluate the results, the proposed multivariate chaotic time series predictor based on multivariate local polynomial model is compared with a univariate predictor with the same numerical data. The simulation results obtained by the Lorenz system show that the prediction mean squares error of the multivariate predictor is much smaller than the univariate one, and is much better than the existing three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squares error is smaller than that of the univariate predictor. 相似文献