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 ...

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...

A feedback Group Method of Data Handling (GMDH)-type neural network algorithm is proposed, and is applied to nonlinear system identification and medical image analysis of liver cancer. In this feedback GMDH-type neural network algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures, such as sigmoid function neural network, ra...

Neural networks with active neurons which selforganize their structure can use inductive sorting-out GMDH algorithms for their neurons. New threshold type GMDH algorithm with polynomial complexity is developed to decrease computing time in case of large input data sample.

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. ...

Genetic Algorithm (GA) is deployed for optimal design of configuration involved in GMDH-type neural networks which is used for modelling of centre deflection, hoop strain and thickness strain of explosive forming process. In this way, a new encoding scheme is presented to genetically design the generalized GMDH-type neural networks in which the connectivity configuration in such networks is not...

Neural networks (NN) are a relatively new option to model growth in animal production systems. One self-organizing submodel of artificial NN is the group method of data handling (GMDH)-type NN. The use of such self-organizing networks has led to successful application of the GMDH algorithm over a broad range of areas in engineering, science, and economics. The present study aimed to apply the G...

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 precursor (p-gMDH) and mature (gMDH) forms we...

In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point de...

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance mea...