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Description: 神经网络gmdh的详细程序，demo是主程序-Neural Networks gmdh detailed procedures, demo is the main program
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Size: 26624 |
Author: cloud |
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Description: gmdh神经网络专用软件knowledgeminer5.0，注意要用中介软件executor才能在windows下使用-gmdh neural network-specific software knowledgeminer5.0, note that the software executor to use an intermediary to use in windows
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Size: 5238784 |
Author: cloud |
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Description: GMDH的MATLAB源代码，是主程序，也就是gmdhbuild.文件。与之配套的测试文件gmdhtest.m、输出误差gmdhpredice.m会接着发出。-GMDH The MATLAB source code, is the main program, which is gmdhbuild. File. Accompanying test file gmdhtest.m, output error will be followed by a gmdhpredice.m.
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Size: 12288 |
Author: songhuaixiang |
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Description: GMDH的MATLAB源代码，是主程序，也就是gmdhbuild.文件。与之配套的测试文件gmdhtest.m、输出误差gmdhpredice.m会接着发出。-GMDH The MATLAB source code, is the main program, which is gmdhbuild. File. Accompanying test file gmdhtest.m, output error will be followed by a gmdhpredice.m.
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Size: 1024 |
Author: songhuaixiang |
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Description: GMDH的MATLAB源代码，是主程序，也就是gmdhbuild.文件。与之配套的测试文件gmdhtest.m、输出误差gmdhpredice.m会接着发出。-GMDH The MATLAB source code, is the main program, which is gmdhbuild. File. Accompanying test file gmdhtest.m, output error will be followed by a gmdhpredice.m.
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Size: 1024 |
Author: songhuaixiang |
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Description: VariReg是一款免费的GMDH软件，解压后记事本上有开发该代码的网址，里面还有丰富的MATLAB编写的GMDH原程序。非常好用。-VariReg is a free GMDH software, decompression after Notepad to develop the code on the URL, there is also a wealth of MATLAB programs written in the original GMDH. Is very easy to use.
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Size: 3421184 |
Author: songhuaixiang |
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Description: GMDH Shell 1.3 setup
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Size: 4722688 |
Author: 宋怀翔 |
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Description: 数据分组处理算法GMDH源代码是自组织数据挖掘的核心算法，具有很强的泛化能力，相比回归分析法可以处理小样本数据-GMDH packet processing algorithms source code is the core of self-organizing data mining algorithm, which has strong generalization ability, compared with regression analysis can deal with small samples
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Size: 2048 |
Author: 张瑛 |
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Description: this is a matlab implementation for gmdh network
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Size: 11264 |
Author: maryamhasan |
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Description: this simple sample of Group Method of Data Handling(GMDH) neural network
main file: GMDH.m-this is simple sample of Group Method of Data Handling(GMDH) neural network
main file: GMDH.m
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Size: 23552 |
Author: amin |
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Description: gmdh神经网络的详细程序，demo是主程序源码。-gmdh detailed procedures for neural networks, demo is the main source.
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Size: 27648 |
Author: 认可 |
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Description: 采用MATLAB编写的GMDH软件，里面有三个.m程序，分别为训练、误差检测、预测输出。很好用。-GMDH using software written in MATLAB, which has three. M steps in the training, error detection, prediction output. Good use.
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Size: 14336 |
Author: 清醒 |
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Description: gmdH的MATLAB源代码，是主程序，也就是gmdhbuild.文件。与之配套的测试文件gmdhtest.m、输出误差gmdhpredice.m会接着发出-gmdH the MATLAB source code is the main program, which is gmdhbuild. file. The related test files gmdhtest.m, will then send the output error gmdhpredice.m
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Size: 2048 |
Author: 李增光 |
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Description: GMDH_main为GMDH主函数；
variable_Combin为输入层初始变量选为x1,x1^2,x1*x2,x2^2,x2时的输入变量矩阵值
variable_select计算X_train训练输入数据,Y_train训练输出数据,X_test测试输入数据,Y_test测试输出数据
Combin为求变量的两两组合
Sym_Combin为求符号变量的两两组合
PE_AIC求每层各神经元的参数估计W，及训练数据在参数估计后估计的输出out_train，测试数据在参数估计后的估计输出out_test，还有与实际比较的误差平方和PESS， 以及准则值AIC
sym_representation求最终的输入输出符号表达式
Criterion_value求准则值-GMDH_main main function for the GMDH variable_Combin initial variables chosen for the input layer, x1, x1 ^ 2, x1* x2, x2 ^ 2, x2 the value of the input variable matrix calculation X_train variable_select training input data, Y_train training output data, X_test test input data, Y_test for the sake of the test output data Combin pairwise combination of variables for the sake of symbolic variables Sym_Combin pairwise combinations PE_AIC neurons find each parameter estimation of the W, and after the training data is estimated in the estimation of output out_train, test data After the parameter estimates of the output estimated out_test, also compared with the actual sum of squared errors PESS, as well as the final criterion value AIC sym_representation seek the input and output values of symbolic expressions Criterion_value find criteria
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Size: 11264 |
Author: 李增光 |
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Description: GMDH _ Nureal Network _ Matlab code
-GMDH _ Nureal Network _ Matlab code
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Size: 1024 |
Author: mehdis |
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Description: A project for function approximation by GMDH neural network (with GUI).
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Size: 12288 |
Author: hamed |
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Description: 基于自组织数据挖掘的多分类器集成选择的程序-Multiple classifiers ensemble selection based on GMDH
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Size: 1771520 |
Author: 肖进 |
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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
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Size: 1467392 |
Author: |
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Description: gmdh代码 训练方法
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1.将数据集分为训练集和测试集。
2.建立输入层
3.建立所有符合5楼条件的神经元（只考虑“攻”和“受”的关系，不考虑具体的权值）。如果前面几层共有N个神经元，那么我们就需要建立N(N-1)/2个神经元。
4.用最小二乘法确定权值
5.计算每一个神经元在测试集上的表现。
6.选取表现最好的K个神经元放在后面一层。（K可以自己选择）
7.重复3,直到网络的性能足够好，或者网络开始过拟合。(GMDH codeTraining methods
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1. Divide the data set into training sets and test sets.
2. Create an input layer
3. Establish all neurons that meet the conditions of the 5th floor (only consider the "attack" and "by" the relationship, regardless of the specific weight). If there are N neurons in the first few layers, then we need to establish N (N-1) / 2 neurons.
4. Determine the weight using the least squares method
5. Calculate the performance of each neuron on the test set.
6. Select the best performing K neurons on the back. (K can choose their own)
7. Repeat 3, until the network performance is good enough, or the network began to fit.)
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Size: 3072 |
Author: adch
|
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Description: By means of gmdh algorithm a model can be represented as set of neurons in which different pairs of them in each layer are connected through a quadratic polynomial and thus produce new neurons in the next layer. Such representation can be used in modelling to map inputs to outputs. The formal defini.
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Size: 4933632 |
Author: FADAIE |
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