Training of RFB neural networks using a full-genetic approach
نویسندگان
چکیده
The efficiency of pattern recognition (PR) systems using RBF neural networks to implement their recognition function, depends a lot by the training algorithms of these neural networks and especially, by the specific techniques (e.g., supervised, clustering techniques etc.) used for RBF center positioning. Having as starting point the basic property of genetic algorithms (GA) to represent global searching tools, a full-genetic approach to assure optimization both connectivity and neural weights of RBF networks is proposed. In order to confirm the broached theoretical aspects and based on real pattern recognition task, a comparative study (as performance level) with others standard RBF training methods and SART neural network is also indicated. Key-Words: RBF neural networks, clustering techniques, genetic algorithms
منابع مشابه
Modeling 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 کاملOptimizing Multiple Response Problem Using Artificial Neural Networks and Genetic Algorithm
This paper proposes a new intelligent approach for solving multi-response statistical optimization problems. In most real world optimization problems, we are encountered adjusting process variables to achieve optimal levels of output variables (response variables). Usual optimization methods often begin with estimating the relation function between the response variable and the control variab...
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 کاملYarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms
Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 Ne. As the first step for modeling, the empirical data is col...
متن کاملModeling of measurement error in refractive index determination of fuel cell using neural network and genetic algorithm
Abstract: In this paper, a method for determination of refractive index in membrane of fuel cell on basis of three-longitudinal-mode laser heterodyne interferometer is presented. The optical path difference between the target and reference paths is fixed and phase shift is then calculated in terms of refractive index shift. The measurement accuracy of this system is limited by nonlinearity erro...
متن کامل