Description: 用小波神经网络来对时间序列进行预测，其中有四个m文件-wavelet neural network to forecast the time series, which document 4 m Platform: |
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Author:stefwing |
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Description: 在用混沌理论和神经网络进行短期负荷预测时，神经网络的输入的选择至关重要，该程序用matlabl实现了基于混沌时间序列的嵌入维数的选择-using chaos theory and neural networks for short-term load forecasts, the neural network is essential to choose an input, The procedure used matlabl achieved a chaotic time series based on the embedding dimension of choice Platform: |
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Author:sunyan |
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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: |
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Author:程正 |
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Description: 资源分配神经网络解决Mackey-Glass时间序列预测函数逼近问题-Neural network to solve the allocation of resources Mackey-Glass time series prediction function approximation problem Platform: |
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Author:吴强 |
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Description: 利用神经网络预测时间序列，以太阳黑子和实际的转子故障信号为例，matlab编程-Using neural network time series prediction to sunspots and the actual rotor fault signal as an example, matlab programming Platform: |
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Author:苏文胜 |
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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: |
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Author:yujian |
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Description: matlab格式源代码。功能：径向基神经网络算法源码和应用于时间序列模型建立和预测问题。-matlab source code format. Function: RBF neural network algorithm source code and applies to time-series model and prediction of the problem. Platform: |
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Author:magic |
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Description: matlab编写的基于混沌时间序列的神经网络预测，包括一步和多步预测算法。-matlab prepared chaotic time series based on the neural network to predict, including step and multi-step prediction algorithm. Platform: |
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Author:jcuaon |
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Description: 一种简单有效的基于动态时变语音识别源码
对于大多数研究者来说,寻找能够匹配二重时间序列信号的最佳途径是很重要的,因为它有许多重要的应用需求.DTW是实现这项工作的显著技术,尤其在语音识别技术领域,在这里一个测试信号被按照参照模板拉伸或压缩,
-Searching for the best path that matches two time-series signals is the main task for many researchers, because of its importance in these applications. Dynamic Time-Warping (DTW) is one of the prominent techniques to accomplish this task, especially in speech recognition systems. DTW is a cost minimisation matching technique, in which a test signal is stretched or compressed according to a reference template.
Although there are other advanced techniques in speech recognition such as the hidden Markov modelling (HMM) and artificial neural network (ANN) techniques, the DTW is widely used in the small-scale embedded-speech recognition systems such as those embedded in cell phones. The reason for this is owing to the simplicity of the hardware implementation of the DTW engine, which makes it suitable for many mobile devices. Additionally, the training procedure in DTW is very simple and fast, as compared with the HMM and ANN rivals.
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Author:宋小小 |
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Description: 用小波神经网络变换对时间序列信号进行预测，并做了测试，效果很好，请参考-Transform using wavelet neural network to predict the time series signal, and do a test with good results, please refer to Platform: |
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Author:hujunhua |
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Description: MATLAB是一种用于科学工程计算的高效率高级语言。MATLAB的基本数据单位是矩阵，它的工具箱已经延伸到了科学研究和工程应用的许多领域。它在诸如一般数值计算、数字信息处理、系统识别、自动控制、振动理论、时序分析与建模、优化设计、神经网络控制、动态仿真系统、金融管理、小波分析、特殊函数和图形领域表现出一般高级语言难以相比的优势，并可以方便地用于几乎所有的科学和工程计算的各方面。-MATLAB is a science and engineering computing for efficient high-level language. MATLAB' s basic data unit is the matrix, and its toolbox has been extended to scientific research and engineering applications in many fields. It is in such general numerical calculation, digital information processing, system identification, automatic control, vibration theory, time series analysis and modeling to optimize design, neural network control, dynamic simulation system, financial management, analysis, special functions and graphics in the field show generally difficult to match the advantages of high-level language, and can be easily used in almost all scientific and engineering computing aspects. Platform: |
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Author:杨小林 |
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Description: 文章展示了一种新的方法用于功率系统中短期负载预测。提出的方案使用混沌时间序列分析基于确定性混沌去捕捉复杂的负载行为特征。确定性的混沌允许我们重构一个时间序列并决定输入的变量个数。这篇文章描述了混沌时间序列对日间功率系统峰值的分析。确定性混沌的非线性图形通过多层感知器的神经网络得到。提出的方案在一个例子中具体阐述。-This paper presents a new approach to short-term load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behavior. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of daily power system peak loads. The nonlinear
mapping of deterministic chaos is identified by the multilayer perceptron of an artificial neural network. The proposed approach is demonstrated in an example. Platform: |
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Author:will |
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Description: 算法大全 全书分30章及2附录（在MATLAB中实现）对常用数学算法进行汇总介绍。
主要包括：线性规划、非线性规划、动态规划、图与网络、排队论、对策论、层次分析法、插值与拟合、数据的统计描述和分析、方差分析、回归分析、微分方程建模、稳定状态模型、常微分方程解法、差分方程模型、马氏链模型、变分法模型、神经网络模型、偏微分方程的数值解、目标规划、模糊数值模型、现代优化算法、时间序列模型、存贮论、经济与金融的优化问题、生产与服务运作管理中的优化问题、灰色系统理论及其应用、多元分析、偏最小二乘回归以及附录-Complete collection of algorithm including 30 chapters and 2 appendices: linear programming, nonlinear programming, dynamic programming, graph and networks, queuing theory, game theory, the level of analysis, interpolation and fitting, statistical description and analysis of data , analysis of variance, regression analysis, differential equations modeling, steady-state model, ordinary differential equation solution, difference equation model, Markov chain model, variational method model, neural network model, the numerical solution of partial differential equations, goal programming, fuzzy numerical model, modern optimization algorithms, time series models, storage theory, the optimization of economic and financial issues, production and service operations management in the optimization problem, gray system theory and its applications, multivariate analysis, partial least squares regression, and Appendix
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Author:商志远 |
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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: |
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Author:wanggen |
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Description: 运用神经网络与时间序列分析对风电功率进行预测的一个matlab程序。-Using neural network and time series analysis forecast wind power Platform: |
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Author:年兴 |
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Description: 利用MATLAB的反馈网络-Elman神经网络处理时间序列问题的功能预测上证股市开盘价。-Elman Feedback network using MATLAB neural network for time series prediction problem function on card stock market opening. Platform: |
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Author:sdu |
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Search - neural network matlab time series