共查询到17条相似文献,搜索用时 46 毫秒
1.
基于复杂非线性系统相空间重构理论,提出了混沌背景中微弱信号检测的神经网络方法,利用神经网络强大的学习和非线性处理能力,建立了混沌背景噪声的一步预测模型,从预测误差中检测淹没在混沌背景噪声中的微弱目标信号(包括周期信号和瞬态信号),研究了混沌背景中存在白噪声时该方法的检测能力,指出了目标信号为瞬态信号和周期信号时检测原理的异同点,最后以Lorenz系统作为混沌背景噪声进行了仿真实验,实验表明该方法能有效地将混沌背景中极其微弱的信号检测出来.
关键词:
混沌
神经网络
信号检测 相似文献
2.
本文提出了利用广义负熵盲检测机理对混沌超宽带系统的进行非相干盲检测的研究方法, 在传输信道状态未知的情况下即可检测出混沌脉冲信号. 该方法能够克服用于超宽带无线电通信的直接混沌通信方案存在的实际应用问题, 能够有效解决未知复杂多径信道问题. 通过仿真结果分析表明, 采用非相干盲检测方法的直接混沌通信超宽带方案, 不需要信道状态信息, 具有很好的分离检测效果. 为未知非理想信道下, 非相干盲检测的混沌超宽带系统提出了新思路. 相似文献
3.
针对深度神经网络训练过程中残差随着其传播深度越来越小而使底层网络无法得到有效训练的问题,通过分析传统sigmoid激活函数应用于深度神经网络的局限性,提出双参数sigmoid激活函数。一个参数保证激活函数的输入集中坐标原点两侧,避免了激活函数进入饱和区,一个参数抑制残差衰减的速度,双参数结合有效的增强了深度神经网络的训练。结合DBN对MNIST数据集进行数字分类实验,实验表明双参数 sigmoid激活函数能够直接应用于无预训练深度神经网络,而且提高了sigmoid激活函数在有预训练深度神经网络中的训练效果。 相似文献
4.
5.
基于双混沌置乱和扩频调制的彩色图像盲水印算法 总被引:5,自引:0,他引:5
提出了一种新的适应彩色图像的盲水印算法,先对宿主图像的绿色分量以8×8像素分块进行离散余弦变换(DCT)变换,用logistic映射生成两个混沌序列,然后用混沌序列置乱加密二值水印图像,并用两个互不相关的伪随机序列扩频调制水印,最后将调制好的水印嵌入到DCT变换域的中频子带系数上,进行分块DCT反变换得到水印化图像.提取水印时,通过比较两个伪随机序列和水印化图像的相关性大小来提取水印,不需要原始图像的参与,为盲提取水印算法.实验结果证明本文算法能有效地抵抗JPEG压缩、加噪、剪切等常见攻击,绿色分量嵌入水印比红色和蓝色分量嵌入水印能更好地抵抗JPEG压缩的攻击. 相似文献
6.
针对水声通信网络中遇到的多用户检测中目标用户的多址干扰等检测问题,提出了基于改进Kalman算法的盲自适应多用户检测算法,解决了多用户检测中的多址干扰对水声通信信道用户变动时的干扰抑制问题。仿真分析分别针对同步多用户、异步多用户通信过程,对比了传统Kalman算法及改进的Kalman算法的性能差异,通过仿真对比表明,改进后的Kalman检算法不需要训练序列即可以实现同步和异步通信状态下的多水声目标用户的盲自适应检测,改进后的算法目标检测的信干比比传统算法最大可提高6dB。新算法对于水下多用户检测、区分,准确、稳定的实现基于CDMA协议的快速水声通信具有重要意义。 相似文献
7.
混沌信号所固有的非周期、宽带频谱和对初值极度敏感等特性使得对这类信号进行盲分离极为困难. 针对这一问题, 提出一种新的盲分离方法, 该方法通过相空间重构来构造代价函数, 将混沌信号的盲分离转化为一个无约束优化问题, 并利用人工蜂群算法进行求解. 不同于现有的独立成分分析方法仅使用混合信号的统计特性来解决分离问题, 该方法能充分利用混合信号内在的动态特性, 因而在处理混沌信号这种确定性信号时能获得更好的分离效果. 此外, 正交矩阵的参数化表示有效地降低了盲分离问题的复杂性, 使优化过程能快速收敛. 实验结果表明, 该方法具有较快的收敛速度和较高的数值精度, 在分离混沌信号时其整体性能优于现有的几种盲分离方法. 同时, 在分离混沌-高斯混合信号的实验中该方法也展现出优异良好的性能, 这表明该文的方法有应用潜力. 相似文献
8.
9.
研究了异结构混沌系统之间的同步控制问题.采用非线性反馈控制方法实现了3D混沌系统和单模激光Lorenz混沌系统之间的混沌同步.根据系统的稳定性理论,得到了非线性反馈控制器的结构和反馈控制增益的取值范围.仿真模拟的结果表明:目标系统和响应系统达到完全同步,两系统状态变量随时间的演化轨迹完全一致,并且误差变量经过短暂的时间序列以后始终平稳地趋于零.仿真模拟的结果证明了这种方法的有效性. 相似文献
10.
11.
Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique
下载免费PDF全文
![点击此处可从《中国物理 B》网站下载免费的PDF全文](/ch/ext_images/free.gif)
The global stability problem of Takagi-Sugeno(T-S) fuzzy Hopfield neural networks(FHNNs) with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler’s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches. 相似文献
12.
13.
14.
In this paper, we focus on the robust adaptive synchronization between two coupled chaotic neural networks with all the parameters unknown and time-varying delay. In order to increase the robustness of the two coupled neural networks, the key idea is that a sliding-mode-type controller is employed. Moreover, without the estimate values of the network unknown parameters taken as an updating object, a new updating object is introduced in the constructing of controller. Using the proposed controller, without any requirements for the boundedness, monotonicity and differentiability of activation functions, and symmetry of connections, the two coupled chaotic neural networks can achieve global robust synchronization no matter what their initial states are. Finally, the numerical simulation validates the effectiveness and feasibility of the proposed technique. 相似文献
15.
A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis(FastICA) method when noise contamination is considerable. 相似文献
16.
17.
Multi-step-prediction of chaotic time series based on co-evolutionary recurrent neural network 总被引:1,自引:0,他引:1
下载免费PDF全文
![点击此处可从《中国物理 B》网站下载免费的PDF全文](/ch/ext_images/free.gif)
This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series. 相似文献