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Group method of data handling

Known as: GMDH, Polynomial neural network 
Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that… 
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Related topics

Related topics

10 relations
Artificial neural networkComplex systemsData miningDeep learning

Broader (1)

Computational statistics

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Multi Criteria Decision Making and Group Method of Data Handling
The main drawback of decision making is that the process depends on subjective inputs. That is why decision becomes inconsistent… 
2012
2012
A GMDH-based fuzzy modeling approach for constructing TS model
2009
2009
Software Reliability Prediction Using Group Method of Data Handling
The main purpose of this paper is to propose the use of Group Method of Data Handling (GMDH) to predict software reliability. The… 
2007
2007
Group Method of Data Handling-Type Neural Network Prediction of Broiler Performance Based on Dietary Metabolizable Energy, Methionine, and Lysine
SUMMARY Artificial neural networks have been shown to be powerful tools for system modeling. One submodel of artificial neural… 
2007
2007
Logistic GMDH-Type Neural Network and its Application to Identification of X-Ray Film Characteristic Curve
2006
2006
Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication
2006
2006
Application of Ridge Polynomial Neural Networks to Financial Time Series Prediction
This paper presents a novel application of ridge polynomial neural network to forecast the future trends of financial time series… 
2003
2003
Natural Gas Prediction Using The Group Method of Data Handling
The flow of natural gas from a system of wells is a highly nonlinear process. In this paper we are taking a time series approach… 
Highly Cited
2003
Highly Cited
2003
Gradient feature extraction for classification-based face detection
1994
1994
New Structure Criteria in Group Method of Data Handling
The task of statist ical modeling can be expressed as shown in Fig. 1. It can be divided into two parts: 1. parameter… 
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