Description: 多步最小二乘法的实现，用于系统识别，采用神经网络识别等方法-multi-step realization of the least squares method, used for system identification, neural network recognition methods Platform: |
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Author:马晋 |
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Description: 多步最小二乘法的实现，用于系统识别，采用神经网络识别等方法-multi-step realization of the least squares method, used for system identification, neural network recognition methods Platform: |
Size: 947200 |
Author:马晋 |
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Description: 一种最小二乘法的RBF神经网络，用于对复杂系统的辨识研究-A least squares method of RBF neural network for identification of complex systems research Platform: |
Size: 1024 |
Author:杨怀申 |
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Description: 提出了一种基于函数联接的感知器神经网络的纹理分类方法.它采用高斯2马尔柯夫随机场模型(GM RF)对纹理进行描述,模型参数即为纹理特征,参数估计采用最小平方误差方法获得.将估计参数作为表达纹理的特征向量,用感知器网络对特征进行分类,并且采用函数联接的方式解决线性不可分问题.对纹理图象进行的实验表明,采用这种方法能够提高学习速度,简化计算过程,并取得较好的纹理分类效果.
-Based on the function connected perceptron neural network texture classification method. It uses 2 Gaussian Markov Random Field Model (GM RF) to describe the texture, the model parameters is the texture feature, parameter estimation using least squares error obtained. the estimated parameters as the expression of texture feature vector, using the characteristics of sensor networks for classification, and the use of function to resolve connection problems can not be separated from linear. of texture images of the experiments show that this approach can enhance the learning speed, to simplify the calculation process and obtain a better effect of texture classification. Platform: |
Size: 285696 |
Author:singro jiang |
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Description: 为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani 型模糊推理规则控制器控制倒立摆系统稳定的基础上,
设计了一种更有效率的基于Sugeno 型模糊推理规则的模糊神经网络控制器。该控制器使用BP 神经网络和最小二乘法的混
合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani 型控制器的仿真对比,
表明该Sugeno 型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。-In order to improve the three-level control of inverted pendulum system response speed and stability, in the design of Mamdani-type fuzzy inference rules of the system controller to control the stability of inverted pendulum on the basis of a more efficient design based on Sugeno-type fuzzy inference rules of fuzzy neural network controller. The controller is the use of BP neural network and hybrid least squares training algorithm parameters can be accurately summed up the amount of input and output fuzzy membership function and fuzzy logic rules. Mamdani-type controller with a simulation comparison shows that the Sugeno-type fuzzy neural network controller for the three-tier control of inverted pendulum system with good stability and fast, as well as a higher control precision. Platform: |
Size: 551936 |
Author:月到风来AA |
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Description: 神经网络和最小二乘支持向量机的软测量技术应用研究-Based on neural network and the least squares support vector machine of soft measurement technology application research Platform: |
Size: 3835904 |
Author:许龙 |
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Description: 【1】随机序列产生程序
【2】白噪声产生程序
【3】M序列产生程序
【4】二阶系统一次性完成最小二乘辨识程序
【5】实际压力系统的最小二乘辨识程序
【6】递推的最小二乘辨识程序
【7】增广的最小二乘辨识程序
【8】梯度校正的最小二乘辨识程序
【9】递推的极大似然辨识程序
【10】Bayes辨识程序
【11】改进的神经网络MBP算法对噪声系统辨识程序
【12】多维非线性函数辨识程序的Matlab程序
【13】模糊神经网络解耦Matlab程序
【14】F-检验法部分程序
-【1】 【2-random sequence generation process white noise generation process】 【3】 M sequence generation process 【4】 to complete a one-time second-order system least-squares identification procedure 【5】 actual pressure system least-squares identification procedure 【6】 Delivery Push the least squares identification procedure augmented 【7】 【8】 least square identification procedures for gradient correction least square identification procedure 【9】 Recursive maximum likelihood identification procedures 【10】 【11】 Bayes identification procedures Improved neural network algorithm MBP noise system identification procedure 【12】 multi-dimensional nonlinear function identification program Matlab program 【13】 fuzzy neural network decoupling Matlab program 【14】 F-test part of the program Platform: |
Size: 7168 |
Author:jshuska |
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Description: This a java implementation of Levenberg-Marquardt algorithm to train properly a neural network. Levenberg-Marquardt, implemented from the general description in Numerical Recipes (NR), then tweaked slightly to mostly match the results of their code. Use for nonlinear least squares assuming Gaussian errors.-This is a java implementation of Levenberg-Marquardt algorithm to train properly a neural network. Levenberg-Marquardt, implemented from the general description in Numerical Recipes (NR), then tweaked slightly to mostly match the results of their code. Use for nonlinear least squares assuming Gaussian errors. Platform: |
Size: 6144 |
Author:Felippe |
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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
Platform: |
Size: 7684096 |
Author:商志远 |
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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: |
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Author: |
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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-
Algorithm Daquan book in the 30 chapter and appendix (in MATLAB) summary of commonly used mathematical algorithms introduced. Include: linear programming, nonlinear programming, dynamic programming, maps and networks, queuing theory, game theory, the Analytic Hierarchy Process, interpolation and fitting, the statistical description and analysis of the data analysis of variance, regression analysis, differential equations built mode, steady-state model, the solution of ordinary differential equations, differential equation model, Markov chain model, the model of the variational method, neural network model, partial differential equations, numerical solution, goal programming, fuzzy numerical model, modern optimization algorithms, time series models stored on the economic and financial optimization problems, optimization problems in production and service operations management, the gray system theory and its applications, multivariate analysis, partial least squares regression and Appe Platform: |
Size: 4254720 |
Author:烈马 |
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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: 《系统辨识与MATLAB仿真.pdf》 侯媛彬 汪梅 王立琦
本书共分8章，第1、2章为辨识的基本概念、理论基础和古典辨识方法；第3至6章为现代辨识内容，其中第3章是最小二乘参数辨识，第4章是梯度校正参数辨识，第5章是极大似然法的参数辨识方法，第6章是自适应参数辨识；第7、8章为复杂的非线性系统的智能辨识和混沌辨识，其中第7章是非线性系统的神经网络辨识；第8章是Volterra辨识方法、复杂系统的混沌现象及其辨识。从第2至7章，各章均包含开发的相应程序及其程序剖析。
-" System Identification and MATLAB simulation. Pdf" Hou Yuanbin Wang Mei Wang Liqi book is divided into eight chapters, Chapters 1 and 2 for the identification of the basic concepts, theoretical foundation and classical identification methods first 3-6 chapters of modern identification content, Chapter 3 is the least squares parameter identification, Chapter 4 is the gradient correction parameter identification, Chapter 5 is the maximum likelihood parameter identification method, Chapter 6 is an adaptive parameter identification Chapters 7 and 8 for complex Intelligent identification of nonlinear systems and chaos identification, which is in Chapter 7 of the neural network nonlinear system identification Chapter 8 is Volterra identification method, complex systems and chaos identification. From the first 2-7 chapters, each chapter contains procedures for the development of appropriate programs and their analysis. Platform: |
Size: 7103488 |
Author:唐小米 |
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Description: 做国赛模拟12年葡萄酒评价问题，自己写的全部问题代码，包括T检验，偏最小二乘分析，典型相关分析，神经网络以及理想评价算法-12 years Wine evaluation problem of Chinese race simulation, all write their own code, including T test, partial least squares analysis, canonical correlation analysis, neural network and the ideal evaluation algorithm Platform: |
Size: 8192 |
Author:小强 |
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Search - least squares neural network