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RBF Network (rbf + network)
Selected AbstractsIntegrative optimization by RBF network and particle swarm optimizationELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 12 2009Satoshi Kitayama Abstract This paper presents a method for the integrative optimization system. Recently, many methods for global optimization have been proposed. The objective of these methods is to find a global minimum of nonconvex function. However, large numbers of function evaluations are required, in general. We utilize the response surface method to approximate function space to reduce the function evaluations. The response surface method is constructed from sampling points. The RBF Network, which is one of the neural networks, is utilized to approximate the function space. Then Particle Swarm Optimization (PSO) is applied to the response surface. The proposed system consists of three parts: (Part 1) generation of the sampling points, (Part 2) construction of response surface by RBF Network, (Part 3) optimization by PSO. By iterating these three parts, it is expected that the approximate global minimum of nonconvex function can be obtained with a small number of function evaluations. Through numerical examples, the effectiveness and validity are examined. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(12): 31,42, 2009; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecj.10187 [source] Artificial neural networks for estimating soil hydraulic parameters from dynamic flow experimentsEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2005G. H. Schmitz Summary Inverse methods are often used for estimating soil hydraulic parameters from experiments on flow of water through soil. We propose here an alternative method using neural networks. We teach a problem-adapted network of radial basis functions (RBF) the relationship between soil parameters and transient flow patterns using a numerical flow model. The trained RBF network accurately identifies soil parameters from flow patterns not contained in the training scenarios. A comparison with the inverse method (Annealing-Simplex) reveals a similarly good prediction by both approaches for randomly perturbed data and data from the real world. Nonetheless, the inverse method showed dependency on initial parameter estimates not required by the RBF network. Training demands moderately more computation and manpower than the inverse technique, but the absolutely stable and simple network application requires negligible resources. Thus, for individual applications, the network approach is slightly surpassed by the Annealing-Simplex method. However, the RBF network has to be trained only once and, subsequently, it can be applied easily and without effort upon any number of laboratory experiments with standardized experimental setups. [source] Application of an Artificial Neural Network for Simulating Robust Plasma-Sprayed Zirconia CoatingsJOURNAL OF THE AMERICAN CERAMIC SOCIETY, Issue 5 2008Ming-Der Jean This article presents the application of the artificial neural network (ANN) of a statistically designed experiment for developing a robust wear-resistant zirconia coating. In this research, experimental design with orthogonal arrays efficiently provides enough information with the least number of experiments, reducing the cost and time. A radial basis function (RBF) network for the wear behavior is adopted. The friction and tribological properties of zirconia coatings were investigated. The microstructural feature of the coatings is also addressed in this study. It is found that the worn volumes of plasma-sprayed zirconia coatings after wear tests are greatly improved by the optimal parameters. The relationships between the microstructure of the worn surface and their properties are examined, and the results reveal a higher wear resistance and a lower worn surface roughness with a large amount of plastic deformations. These wear resistant structures formed as a result of a dense lamellar formation during sprayed zirconia coatings. The RBF network can be established efficiently. A comparison of the predicted results with that of the RBF network and the Taguchi method predictor shows average errors of 2.735% and 9.191% for the RBF network and the Taguchi method, respectively. It is experimentally confirmed that the RBF network predictions are in agreement with the experiments, and it can be reliably used for the prediction of wear for plasma sprayings. The experimental results demonstrate that the RBF network used for a statistically designed experiment is an effective, efficient, and intelligent approach for developing a robust, high efficiency, and high-quality zirconia coating process. [source] A Cartesian grid technique based on one-dimensional integrated radial basis function networks for natural convection in concentric annuliINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 12 2008N. Mai-Duy Abstract This paper reports a radial basis function (RBF)-based Cartesian grid technique for the simulation of two-dimensional buoyancy-driven flow in concentric annuli. The continuity and momentum equations are represented in the equivalent stream function formulation that reduces the number of equations from three to one, but involves higher-order derivatives. The present technique uses a Cartesian grid to discretize the problem domain. Along a grid line, one-dimensional integrated RBF networks (1D-IRBFNs) are employed to represent the field variables. The capability of 1D-IRBFNs to handle unstructured points with accuracy is exploited to describe non-rectangular boundaries in a Cartesian grid, while the method's ability to avoid the reduction of convergence rate caused by differentiation is instrumental in improving the quality of the approximation of higher-order derivatives. The method is applied to simulate thermally driven flows in annuli between two circular cylinders and between an outer square cylinder and an inner circular cylinder. High Rayleigh number solutions are achieved and they are in good agreement with previously published numerical data. Copyright © 2007 John Wiley & Sons, Ltd. [source] Artificial neural network modeling of O2 separation from air in a hollow fiber membrane moduleASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2008S. S. Madaeni Abstract In this study artificial neural network (ANN) modeling of a hollow fiber membrane module for separation of oxygen from air was conducted. Feed rates, transmembrane pressure, membrane surface area, and membrane permeability for the present constituents in the feed were network input data. Output data were rate of permeate from the membrane, the amount of N2 in the remaining flow, and the amount of O2 in the permeate flow. Experimental data were obtained from software developed by Research Institute of Petroleum Industry (RIPI). A part of the data generated by this software was confirmed by experimental results available in literature. Two third of the data were employed for training ANNs. Based on different training algorithms, radial basis function (RBF) was found as the best network with minimum training error. Generalization capability of best RBF networks was checked by one third of unseen data. The network was able to properly predict new data that incorporate excellent performance of the network. The developed model can be used for optimization and online control. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd. [source] | ||||||||||