In the case of substantial noise, i.e., for inaccurate and incomplete data, the use of the Group Method of Data Handling (GMDH) algorithm leads to sharp and rather deep minimums of dependency of external criterion of accuracy measured on testing sample on the complexity of model structure. This minimum indicates the optimal model. In practice, however, if the noise is just noticeable, i.e., if ...

This paper proposes a new intrusion detection methodology based on hybrid of differential evolution (DE) and group method of data handling (GMDH). It focuses on intrusion detection based on system call sequences using text processing techniques. The hybrid DE-GMDH is used to classify a process as either normal or abnormal. This work presents the application of PCA and hybrid DE-GMDH to modeling...

in this study, a model for estimating the nfs thermal conductivity by using a gmdh-pnn has been investigated. nfs thermal conductivity was modeled as a function of the nanoparticle size, temperature, nanoparticle volume fraction and the thermal conductivity of the base fluid and nanoparticles. for this purpose, the developed network contains 8 layers with 2 inputs in each layer and also trainin...

An improved neuro-fuzzy based group method of data handling using the particle swarm optimization (NF-GMDH-PSO) is developed as an adaptive learning network to predict the localized scour downstream of a sluice gate with an apron. The input characteristic parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, and the flow conditions upst...

In this paper, we propose new computational intelligence sequential hybrid architectures involving Genetic Programming (GP) and Group Method of Data Handling (GMDH) viz. GP-GMDH, GMDH-GP and recurrent architecture for Genetic Programming (GP) for software cost estimation. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are also developed. We also ...

An improved neuro-fuzzy based group method of data handling using the particle swarm optimization (NF-GMDH-PSO) is developed as an adaptive learning network to predict the localized scour downstream of a sluice gate with an apron. The input characteristic parameters affecting the scour depth are the sediment size and its gradation, apron length, sluice gate opening, and the flow conditions upst...

Accurate prediction of water level (WL) is essential for the optimal management different resource projects. The development a reliable model WL remains challenging task in resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict d...