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» Learning fault-tolerance in Radial Basis Function Networks
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IJON
2006
89views more  IJON 2006»
13 years 7 months ago
Flexible kernels for RBF networks
In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. The method provides an extra degree of flexibility to the kernel structure. This ...
André O. Falcão, Thibault Langlois, ...
ESANN
2001
13 years 8 months ago
Transfer functions: hidden possibilities for better neural networks
Abstract. Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast learning of neural networks. Families of parameterized transfer functions provide...
Wlodzislaw Duch, Norbert Jankowski
ICC
2007
IEEE
155views Communications» more  ICC 2007»
14 years 1 months ago
Automatic Digital Signal Types Recognition Using SI-NN and HOS
— Recognition of digital signal type is an important topic for various applications. In this paper a method is presented that identifies different types of digital signals. This ...
Ataollah Ebrahimzadeh, Mehrdad Ardebilipour, Alire...
ICIP
2004
IEEE
14 years 9 months ago
Identification of insect damaged wheat kernels using transmittance images
We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as th...
A. Enis Çetin, Tom Pearson, Zehra Cataltepe
CORR
2010
Springer
155views Education» more  CORR 2010»
13 years 5 months ago
A QoS Provisioning Recurrent Neural Network based Call Admission Control for beyond 3G Networks
The Call admission control (CAC) is one of the Radio Resource Management (RRM) techniques that plays influential role in ensuring the desired Quality of Service (QoS) to the users...
H. S. Ramesh Babu, Gowrishankar, P. S. Satyanaraya...