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ISCI
2008
95views more  ISCI 2008»
13 years 11 months ago
Modified constrained learning algorithms incorporating additional functional constraints into neural networks
In this paper, two modified constrained learning algorithms are proposed to obtain better generalization performance and faster convergence rate. The additional cost terms of the ...
Fei Han, Qing-Hua Ling, De-Shuang Huang
NC
2002
13 years 11 months ago
Parallel evolutionary training algorithms for "hardware-friendly" neural networks
In this paper, Parallel Evolutionary Algorithms for integer weight neural network training are presented. To this end, each processor is assigned a subpopulation of potential solut...
Vassilis P. Plagianakos, Michael N. Vrahatis
IJCAI
1997
14 years 23 days ago
An Effective Learning Method for Max-Min Neural Networks
Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ m...
Loo-Nin Teow, Kia-Fock Loe
ESANN
2003
14 years 25 days ago
Approximation of Function by Adaptively Growing Radial Basis Function Neural Networks
In this paper a neural network for approximating function is described. The activation functions of the hidden nodes are the Radial Basis Functions (RBF) whose parameters are learn...
Jianyu Li, Siwei Luo, Yingjian Qi
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
14 years 3 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
ISCAS
2005
IEEE
214views Hardware» more  ISCAS 2005»
14 years 5 months ago
Blind separation of statistically independent signals with mixed sub-Gaussian and super-Gaussian probability distributions
— In the context of Independent Component Analysis (ICA), we propose a simple method for online estimation of activation functions in order to blindly separate instantaneous mixt...
Muhammad Tufail, Masahide Abe, Masayuki Kawamata
ICANN
2007
Springer
14 years 5 months ago
Deformable Radial Basis Functions
Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical consider...
Wolfgang Hübner, Hanspeter A. Mallot