GMDH algorithms for complex systems modelling

Trade-offs in Optimization of GMDH-Type Neural Networks for Modelling of A Complex Process

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 ...

GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms

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...

Notes on statistical modelling for complex systems

These notes are an outline of the concepts, terms, and notation used in the statistical treatment of model-based inference for complex systems. They are a synthesis of a wide and rapidly-developing literature, and only the simplest situations are covered. Having said that, the ‘all-gaussian’ approach that is illustrated here requires only minor generalisations to be state-of-the-art in current ...

Agent-oriented Modelling for Complex Systems

channel estimation for mimo-ofdm systems

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