Description: 基于Volterra滤波器混沌时间序列多步预测
作者：陆振波，海军工程大学
欢迎同行来信交流与合作，更多文章与程序下载请访问我的个人主页
电子邮件：luzhenbo@sina.com
个人主页：luzhenbo.88uu.com.cn
参考文献：
1、张家树.混沌时间序列的Volterra自适应预测.物理学报.2000.03
2、Scott C.Douglas, Teresa H.-Y. Meng, Normalized Data Nonlinearities for LMS Adaptation. IEEE Trans.Sign.Proc. Vol.42 1994
文件说明：
1、original_MultiStepPred_main.m 程序主文件,直接运行此文件即可
2、original_train.m 训练函数
3、original_test.m 测试函数
4、LorenzData.dll 产生Lorenz离散序列
5、normalize_1.m 归一化
6、PhaSpaRecon.m 相空间重构
7、PhaSpa2VoltCoef.dll 构造 Volterra 自适应 FIR 滤波器的输入信号矢量 Un
8、TrainTestSample_2.m 将特征矩阵前 train_num 个为训练样本，其余为测试样本
9、FIR_NLMS.dll NLMS自适应算法-based Volterra filters chaotic time series multi-step forecast Author : bo, the Navy Engineering from the University of peer welcome exchanges and cooperation, more and download articles please visit my personal web page e-mail : luzhenbo@sina.com WEBSITE : luzhenbo.88uu.com.cn References : 1, and Zhang Shu. the chaotic time series Volterra adaptive prediction. physics reported .2000.03 2, Scott C. Douglas, H.-Y. Teresa Meng, Normalized Data for LMS Adaptation Nonlinearities. IEEE Trans.Sign.Proc . Timing 1994 document : 1, original_MultiStepPred_main.m procedures master file directly run this document can be 2, 3 original_train.m training function, the function tests original_test.m 4, LorenzData.dll have Lorenz five discrete sequence, a normalize_1.m naturalization of six, PhaSpaRecon.m Platform: |
Size: 11847 |
Author:陆振波 |
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Description: 基于Volterra滤波器混沌时间序列多步预测
作者：陆振波，海军工程大学
欢迎同行来信交流与合作，更多文章与程序下载请访问我的个人主页
电子邮件：luzhenbo@sina.com
个人主页：luzhenbo.88uu.com.cn
参考文献：
1、张家树.混沌时间序列的Volterra自适应预测.物理学报.2000.03
2、Scott C.Douglas, Teresa H.-Y. Meng, Normalized Data Nonlinearities for LMS Adaptation. IEEE Trans.Sign.Proc. Vol.42 1994
文件说明：
1、original_MultiStepPred_main.m 程序主文件,直接运行此文件即可
2、original_train.m 训练函数
3、original_test.m 测试函数
4、LorenzData.dll 产生Lorenz离散序列
5、normalize_1.m 归一化
6、PhaSpaRecon.m 相空间重构
7、PhaSpa2VoltCoef.dll 构造 Volterra 自适应 FIR 滤波器的输入信号矢量 Un
8、TrainTestSample_2.m 将特征矩阵前 train_num 个为训练样本，其余为测试样本
9、FIR_NLMS.dll NLMS自适应算法-based Volterra filters chaotic time series multi-step forecast Author : bo, the Navy Engineering from the University of peer welcome exchanges and cooperation, more and download articles please visit my personal web page e-mail : luzhenbo@sina.com WEBSITE : luzhenbo.88uu.com.cn References : 1, and Zhang Shu. the chaotic time series Volterra adaptive prediction. physics reported .2000.03 2, Scott C. Douglas, H.-Y. Teresa Meng, Normalized Data for LMS Adaptation Nonlinearities. IEEE Trans.Sign.Proc . Timing 1994 document : 1, original_MultiStepPred_main.m procedures master file directly run this document can be 2, 3 original_train.m training function, the function tests original_test.m 4, LorenzData.dll have Lorenz five discrete sequence, a normalize_1.m naturalization of six, PhaSpaRecon.m Platform: |
Size: 11264 |
Author:陆振波 |
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Description: 在分析最小均方自适应滤波器(LMSAF)均方误差(MSE)的收敛性时，文献常用统计自相关矩阵代替瞬时自相关矩阵以简化分析，由此得出的收敛条件比较粗糙。本程序指出：不相关高斯输入情况下，无需如上简化，可依据高斯阶矩因式分解定理得到确切的MSE收敛条件，相应的失调表式能更准确地预报失调-In the analysis of LMS adaptive filter (LMSAF) the mean square error (MSE) convergence, the literature commonly used statistical correlation matrix instead of the instantaneous correlation matrix to simplify the analysis, it follows that the convergence conditions are relatively rough. This procedure pointed out: not related to the Gaussian input case, no simplification above may be based on the Gaussian moment factoring theorem to the exact order of MSE convergence conditions, the corresponding offset table style to a more accurate prediction of disorder Platform: |
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Author:王凤 |
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Description: 基于LMS自适应算法的信号预测，可用于图像处理，可用于语音信号处理-LMS adaptive algorithm based on signal prediction, can be used for image processing, can be used for speech signal processing Platform: |
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Author:Jason |
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Description: 格-梯型结构的LMS算法，按照最小均方准则，设计出阶数和时间分别递推的自适应滤波器，可以进行梯型滤波，又具有格型预测前后节独立、收敛快速的优点，但计算量大。-Grid- LMS algorithm in a ladder-type structure, in accordance with the minimum mean square criterion, design order and time adaptive recursive filter, ladder filter, and lattice prediction independent front and rear section, and fast convergence.but computationally intensive. Platform: |
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Description: MATLAB codes for adaptive filtering using least mean square, nominal LMS and Wiener filter using forward linear prediction and backward linear prediction. Platform: |
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Author:MHQ
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