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basis functions , Cambridge University Press, Cambridge, 2004. Buchmann M. D. Radial basis functions 2004

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Morse B., Yoo T., Rheingans P., Chen D., Subramanian K. Interpolating implicit surfaces from scattered surface data using compactly supported radial basis functions, Shape Modeling International , IEEE Computer Society Press, May 2001, pp. 89

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. , 133 , 1 – 8 . [16] Liu J. ( 2013 ), Radial basis function neural network control for mechanical systems . Springer , London

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Central European Geology
Authors: Ana Brcković, Monika Kovačević, Marko Cvetković, Iva Kolenković Močilac, David Rukavina, and Bruno Saftić

et al. 1986 ). The radial-basis function (RBF) network is also a commonly used neural network. It is more successfully and frequently applied in solving classification problems than in solving prediction problems ( Cvetković et al. 2014

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controller, an adaptive self-tune function K ( t ) is designed based on the modified feed-forward radial basis function (RBF) neural network. In general, the basic structure of the RBF neural network consists of three layers (input layer, hidden layer and

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Summary  

In recent years, with the attention to the radial-basis function by mathematicians, more and more research is concentrated on the Gaussian cardinal interpolation. The main purpose of this paper is to discuss the asymptotic behavior of Lebesgue constants of the Gaussian cardinal interpolation operator ℒλ from l (ℤ) into L (ℝ), that is, ∥ℒλ1. We obtain the strong asymptotic estimate of the Lebesgue constants which improves the results of Riemenschneider and Sivakumar in [11].

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Abstract  

An electronic nose utilising an array of six-bulk acoustic wave polymer coated Piezoelectric Quartz (PZQ) sensors has been developed. The nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), Non-virgin olive oil (OI) and Sunflower oil (SFO), were used over a period of 30 days. The sensor responses were then analysed producing an architecture for the Radial Basis Function Artificial Neural Network (RBF). It was found that the RBF results were excellent, giving classifications of above 99% for the vegetable oil test samples.

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A simple and rapid capillary electrophoretic procedure for analysis of matrine and oxymatrine in Kushen medicinal preparations has been developed and optimized. Orthogonal design was used to optimize the separation and detection conditions for the two active components. Phosphate concentration, applied potential, organic modifier content, and buffer pH were selected as variable conditions. The optimized background electrolyte contained 70 mM sodium dihydrogen phosphate and 30% acetonitrile at pH 5.5; the separation potential was 20 kV. Each analysis was complete within 5 min. Regression equations revealed linear relationships (r > 0.999) between peak area and amount for each component. The detection limits were 1.29 μg mL−1 for matrine and 1.48 μg mL−1 for oxymatrine. The levels of the two active compounds in two kinds of traditional Chinese medicinal preparation were easily determined with recoveries of 96.57–106.26%. In addition, multiple linear regression and a non-linear model using a radial basis function neural network approach were constructed for prediction of the migration time of oxymatrine. The predicted results were in good agreement with the experimental values, indicating that a radial basis function neural network is a potential means of prediction of separation time in capillary electrophoresis.

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Being interested in (rotation-)invariant pseudodifferential equations of satellite problems corresponding to spherical orbits, we are reasonably led to generating kernels that depend only on the spherical distance, i.\,e., in the language of modern constructive approximation form spherical radial basis functions. In this paper approximate identities generated by such (rotation-invariant) kernels which are additionally locally supported are investigated in detail from theoretical as well as numerical point of view. So-called spherical difference wavelets are introduced. The wavelet transforms are evaluated by the use of a numerical integration rule, that is based on Weyl's law of equidistribution. This approximate formula is constructed such that it can cope with millions of (satellite) data. The approximation error is estimated on the orbital sphere. Finally, we apply the developed theory to the problems of satellite-to-satellite tracking (SST) and satellite gravity gradiometry (SGG).

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In the paper hardware implemented artificial neural network used for trajectory control of a mobile robot is presented. As a controller RBF (Radial Basis Function) type hardware implemented artificial neural network has been used. There are presented the structure of the neural network implemented on an FPGA system, the main modules of the system, the on chip training.As a robot, a mobile robot with three wheels has been chosen. The left and right side wheels are driven by DC motors and the third one is a free-wheel. The robot position is determined from the image sequence captured by a camera. Finally, measurement results based on the robot model and on the real system will be presented.

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