Description: 为了提高使用精度，研究了某型号MEMS陀螺仪的随机漂移模型。采用游程检验法分析了
该陀螺仪随机漂移数据的平稳性，并根据该漂移为均值非平稳、方差平稳的随机过程的结论，
采用梯度径向基（RBF）神经网络对漂移数据进行了建模。实验结果表明：相比经典RBF网络模
型而言，这种方法建立的模型能更好地描述MEMS陀螺仪的漂移特；相对于季节时间序列模型而
言，其补偿效果提高了大约15％。-In order to improve accuracy, to study a particular model of the MEMS gyroscope random drift model. Using run-length analysis of the test gyro random drift data stationarity, and in accordance with the drift for the average non-stationary, the variance of the random process a smooth conclusion, the use of gradient radial basis (RBF) neural network drift data to build mode. The experimental results show that: compared to the classical RBF network model, this method of establishing a model to better describe the MEMS gyroscope drift special compared with the seasonal time series model, the effect of their compensation increased by approximately 15. Platform: |
Size: 129024 |
Author:程正 |
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Description: 利用灰色系统进行预测的几篇好论文:
BP神经网络_灰色系统联合模型预测软基沉降量 非线性时间序列神经网络预测方法的研究及应用 股票投资价值灰色马尔可夫预测 股票投资价值灰色系统模型及应用 灰色关联神经网络模型在股指预测中的应用 灰色理论与模型及在车辆拥有量预测中的应用 灰色神经网络交通事故预测比较 灰色神经网络预测模型的应用 灰色-神经网络综合预测模型-Gray prediction system using a few good papers: BP neural network system _ a joint model gray soft ground settlement prediction of nonlinear time series prediction method of neural network research and application of the gray value of equity investments Markov prediction value of the equity investments of the gray system Application of gray relational model and neural network model in forecasting stock gray theory and model and prediction of vehicle ownership in the application of gray neural network traffic prediction compare gray neural network prediction model of the application of gray- the integrated neural network prediction model Platform: |
Size: 883712 |
Author:yujian |
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Description: 基于长记性特征的时间序列预测模型，很好用，准确度优于普通神经网络，我自己一直在用-Characteristics based on long memory time series forecasting model, the good, the accuracy is better than an ordinary neural network, I have been using Platform: |
Size: 5120 |
Author:李文兵 |
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Description: A hybrid least squares support vector
machines and GMDH approach for river
fl ow forecasting-This paper proposes a novel hybrid forecasting model, which combines the group
method of data handling (GMDH) and the least squares support vector machine
(LSSVM), known as GLSSVM. The GMDH is used to determine the useful input vari-
ables for LSSVM model and the LSSVM model which works as time series forecasting. 5
In this study the application of GLSSVM for monthly river fl ow forecasting of Selangor
and Bernam River are investigated. The results of the proposed GLSSVM approach
are compared with the conventional artifi cial neural network (ANN) models, Autoregres-
sive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the
long term observations of monthly river fl ow discharge. The standard statistical, the 10
root mean square error (RMSE) and coe ffi cient of correlation (R) are employed to eval-
uate the performance of various models developed. Experiment result indicates that
the hybrid model was powerful tools to mo Platform: |
Size: 1467392 |
Author: |
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Description: 针对神经网络的瓦斯预测模型存在的泛化性能差且存在易陷入局部最优的缺点，提出了
基于最小二乘支持向量机(LS-SVM)时间序列瓦斯预测方法．由于标准最小二乘支持向量机
(L孓SVM)要求样本误差分布服从高斯分布，且标准LS-SVM丧失鲁棒性与稀疏性等特点，提出
了基于加权LS-SVM的瓦斯时间序列预测的方法，从而提高了标准L孓SVM模型的鲁棒性．其
中时间序列的嵌入维数与延迟时间采用了微熵率最小原则进行选取，在此基础上给出了基于加
权L孓SVM实现多步时间序列预测的算法实现步骤．最后利用MATLAB 7．1对其进行仿真研
究，通过鹤壁十矿1个突出工作面的瓦斯涌出数据实例对模型进行了验证．结果表明，加权
SVM模型比标准的L§SVM明显提高了鲁棒性，可较好地实现时间序列数据的多步预测．-The neural network gas prediction model is poor in generalization performance and
easy in fafling into the local optimal value．In order to overcome these shortcomings，we pro—
pose the time series gas prediction method of least squares support vector machine(L§SVM)．
However，in the LS-SVM case，the sparseness and robustness may lose，and the estimation of
the support values iS optimal only in the case of a Gaussian distribution of the error variables．
So，this paper proposes the weighted L孓SVM tO overcome these tWO drawbacks．Meanwhile，
the optimal embedding dimension and delay time of time series are obtained by the smallest dif—
ferential entropy method．On this basis，multi-step time series prediction algorithm steps are
given based on the weighted LS-SVM．Finally，the data of gas outburst in working face of Hebi
lOth mine iS adopted to validate this model．The results show that the predict effect of shortterm
the face gas emission is better using the weighted LS-SVM model than using Platform: |
Size: 490496 |
Author:wanggen |
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Description: 能量熵的计算，IMC-PID是利用内模控制原理来对PID参数进行计算，时间序列数据分析中的梅林变换工具，基于chebyshev的水声信号分析，包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，BP神经网络用于函数拟合与模式识别，脉冲响应的相关分析算法并检验，基于互功率谱的时延估计。- Energy entropy calculation, The IMC- PID is using the internal model control principle for PID parameters is calculated, Time series data analysis Mellin transform tool, Based chebyshev underwater acoustic signal analysis, Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm, BP neural network function fitting and pattern recognition, Related impulse response analysis algorithm and inspection, Based on the time delay estimation of power spectrum. Platform: |
Size: 13312 |
Author:wjtghjj |
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Description: 合成孔径雷达（SAR)目标成像仿真，粒子图像分割及匹配均为自行编制的子例程，isodata 迭代自组织的数据分析，时间序列数据分析中的梅林变换工具，一些自适应信号处理的算法，关于神经网络控制，IMC-PID是利用内模控制原理来对PID参数进行计算。- Synthetic Aperture Radar (SAR) imaging simulation target, Particle image segmentation and matching subroutines themselves are prepared, Isodata iterative self-organizing data analysis, Time series data analysis Mellin transform tool, Some adaptive signal processing algorithms, On neural network control, The IMC- PID is using the internal model control principle for PID parameters is calculated. Platform: |
Size: 9216 |
Author:bhjpgvzk |
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Description: work, I have implemented a neuro-fuzzy neural network using Locally Linear Model Tree learning algorithm in order to predict chaotic time-series. Platform: |
Size: 17408 |
Author:土~ |
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Search - time series model using neural network