Trade-offs in Optimization of GMDH-Type Neural Networks for Modelling of A Complex Process
نویسندگان
چکیده
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 neural networks in which the connectivity configurations in such networks are not limited to adjacent layers. Multi-objective EAs (non–dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto optimization of such GMDH-type neural networks. Optimal Pareto fronts are obtained which exhibit the trade-off between pair of conflicting objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural networks models for such complex process. Key-Words:Pareto optimization, GAs, GMDH, Modelling
منابع مشابه
Using Neural Networks and Genetic Algorithms for Modelling and Multi-objective Optimal Heat Exchange through a Tube Bank
In this study, by using a multi-objective optimization technique, the optimal design points of forced convective heat transfer in tubular arrangements were predicted upon the size, pitch and geometric configurations of a tube bank. In this way, the main concern of the study is focused on calculating the most favorable geometric characters which may gain to a maximum heat exchange as well as a m...
متن کاملPrediction of Deformation of Circular Plates Subjected to Impulsive Loading Using GMDH-type Neural Network
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...
متن کاملModeling and Multi-Objective Optimization of Stall Control on NACA0015 Airfoil with a Synthetic Jet using GMDH Type Neural Networks and Genetic Algorithms
This study concerns numerical simulation, modeling and optimization of aerodynamic stall control using a synthetic jet actuator. Thenumerical simulation was carried out by a large-eddy simulation that employs a RNG-based model as the subgrid-scale model. The flow around a NACA0015 airfoil, including a synthetic jet located at 10 % of the chord, is studied under Reynolds number Re = 12.7 × 106 a...
متن کاملPrediction of pore facies using GMDH-type neural networks: a case study from the South Pars gas field, Persian Gulf basin
The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalanformations in the South Pars gas field. In the first step, pore facies were determined based on Mercury Injection Capillary Pressure (MICP) data incorporation with the Hierarchical Clustering Analysis (HCA) method. In the next step, polynomial meta...
متن کاملPareto Optimization of Two-element Wing Models with Morphing Flap Using Computational Fluid Dynamics, Grouped Method of Data handling Artificial Neural Networks and Genetic Algorithms
A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag ...
متن کامل