Group Method of Data Handling (GMDH)-type neural network algorithms are the heuristic self organization method for the modelling of complex systems. GMDH algorithms are utilized for a variety of purposes, examples include identification of physical laws, the extrapolation of physical fields, pattern recognition, clustering, the approximation of multidimensional processes, forecasting without mo...

doi:10.1111/j.1742-4658.2004.04475.x Many organelle enzymes coded for by nuclear genes have N-terminal sequences, which directs them into the organelle (precursor) and are removed upon import (mature). The experiments described below characterize the differences between the precursor and mature forms of watermelon glyoxysomal malate dehydrogenase. Using recombinant protein methods, the precurso...

The objective of this study was to apply an improved Group Method of Data Handling (GMDH) network model for prediction of debris flow by integrating dynamic rainfall data and environmental factors. The rainfall data were collected from weather information, and the environmental data were extracted from RS, GIS, drilling data, and geophysical data. The input variables used in the SAGA-GMDH model...

The group method of data handling (GMDH) and differential evolution (DE) population-based algorithm are two well-known nonlinear methods of mathematical modeling. In this paper, both methods are explained and a new design methodology which is a hybrid of GMDH and DE is proposed. The proposed method constructs a GMDH network model of a population of promising DE solutions. The new hybrid impleme...

In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability. The effectiveness of the models is compared with that of well-known machine learning techniques viz. Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Backpropagation Neural Network (BPNN), Counter Propaga...

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back-prop and...

Evolutionary Algorithms (EAs) are deployed for multi-objective Pareto optimal design of Group Method of Data Handling (GMDH)-type neural networks that have been used for modelling of a complex process (such as explosive cutting process) using some input-output experimental data. In this way, EAs with a new encoding scheme is firstly presented to evolutionary design of the generalized GMDH-type ...

This survey deals with up-to-date results in the field of hybrid algorithms development of GMDH-type Neural Networks (GMDH-NN) and other methods of Artificial Intelligence (AI) which are successfully used for solving complex economic problems. Such hybrid algorithms are now only in its early stage of active research. General characteristics and main weaknesses of GMDH-NN are firstly presented. ...

The GMDH MIA algorithm uses linear regression for adaptation. We show that Gauss-Markov conditions are not met here and thus estimations of network parameters are biased. To eliminate this we propose to use cloning of neuron parameters in the GMDH network with genetic selection and cloning (GMC GMDH) that can outperform other powerful methods. It is demonstrated on tasks from the Machine Learni...

0957-4174/$ see front matter 2013 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2013.01.060 ⇑ Tel.: +385 1 4561191. E-mail address: [email protected] The main disadvantage of self-organizing polynomial neural networks (SOPNN) automatically structured and trained by the group method of data handling (GMDH) algorithm is a partial optimization of model weights as the GMDH algorithm optimizes on...