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Representation of finite state automata in Recurrent Radial Basis Function networks

Paolo Frasconi, Marco Gori, Marco Maggini, Giovanni Soda
1996 Machine Learning  
In this paper, we propose some techniques for injecting finite state automata into Recurrent Radial Basis Function networks (R2BF).  ...  A technique is suggested for forcing the learning process to develop automata representations that is based on adding a proper penalty function to the ordinary cost.  ...  The radial basis functions considered in this paper are based on Gaussian functions as proposed in (Moody & Darken, 1989 ). 2.  ... 
doi:10.1007/bf00116897 fatcat:ptijve735rg2hgikug4rgxh63u

Non-Linear Equalizers That Estimate Error Rates During Reception

Jesus Cid-Sueiro, A.R. Figueiras Vidal
1998 Zenodo  
The Radial Basis Function (RBF) networks are one of the most ecient neural-network-based equalizers [1, 4] .  ...  The optimal symbol-by-symbol detectors select the symbol which maximizes the \a posteriori" probability of being equal to the transmitted symbol: decisions fb s k g are given by b s k d = arg n max i fPrfs  ... 
doi:10.5281/zenodo.36961 fatcat:n274nzgv5jgkhlh5wds5quveza

Computational complexity comparison of feedforward/radial basis function/recurrent neural network-based equalizer for a 50-Gb/s PAM4 direct-detection optical link

Zhaopeng Xu, Chuanbowen Sun, Tonghui Ji, Jonathan H. Manton, William Shieh
2019 Optics Express  
The four types are feedforward neural networks (F-NN), radial basis function neural networks (RBF-NN), auto-regressive recurrent neural networks (AR-RNN) and layer-recurrent neural networks (L-RNN).  ...  We also demonstrate that only a few tens of multiplications per symbol are needed for NN-based equalizers to guarantee a good BER performance.  ...  F-NN, and the commonly-used activation function for radial basis hidden neurons is f [1] (x) = e −x 2 .  ... 
doi:10.1364/oe.27.036953 pmid:31873466 fatcat:iptlnts4azfolkdwlb2ctrn2ua

Fully complex extreme learning machine

Ming-Bin Li, Guang-Bin Huang, P. Saratchandran, N. Sundararajan
2005 Neurocomputing  
The simulation results show that the ELM equalizer significantly outperforms other neural network equalizers such as the complex minimal resource allocation network (CMRAN), complex radial basis function  ...  neural networks in terms of generalization and learning speed has been proposed by Huang et al.  ...  Real-valued neural network models such as feedforward neural networks, radial basis function (RBF) networks and recurrent neural networks have been successfully used for solving equalization problems as  ... 
doi:10.1016/j.neucom.2005.03.002 fatcat:arf4lq2chrbhdeu7zif7unvboa

Spatial Intuition in Elementary Arithmetic: A Neurocomputational Account

Qi Chen, Tom Verguts, Timothy Ravasi
2012 PLoS ONE  
functions for performing spatial transformations.  ...  To address this issue, we combine two earlier models for parietal cortex: A model we recently proposed on number-space interactions and a modeling framework of parietal cortex that implements radial basis  ...  Acknowledgments The authors thank Wim Fias, Wim Gevers, and André Knops for useful comments on this work, and André Knops for the use of his data.  ... 
doi:10.1371/journal.pone.0031180 pmid:22348052 pmcid:PMC3278421 fatcat:tl2n6pqtuzh6pf5pssekdt2udu

Radial basis function neural network for 2 satisfiability programming

Shehab Alzaeemi, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Saratha Sathasivam, Mustafa Mamat
2020 Indonesian Journal of Electrical Engineering and Computer Science  
The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function  ...  <span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process.  ...  ACKNOWLEDGMENT This research is supported by Universiti Sains Malaysia and Fundamental Research Scheme (FRGS) (6711689) by Ministry of Higher Education Malaysia.  ... 
doi:10.11591/ijeecs.v18.i1.pp459-469 fatcat:xdo3pv6navcvlpgjhbswon3wqy

Neural networks-based turbo equalization of a satellite communication channel

Hasan Abdulkader, Bouchra Benammar, Charly Poulliat, Marie-Laure Boucheret, Nathalie Thomas
2014 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)  
RBF-BASED TURBO EQUALIZER Radial basis function neural networks have gained much interest in engineering applications.  ...  Based on a Volterra model of the satellite non linear communication channel, we derive a soft input soft output (SISO) radial basis function (RBF) equalizer that can be used in an iterative equalization  ... 
doi:10.1109/spawc.2014.6941914 dblp:conf/spawc/AbdulkaderBPBT14 fatcat:ozqq3acohvf43j7r4ndaz3ejry

Complex discriminative learning Bayesian neural equalizer

Mirko Solazzi, Aurelio Uncini, Elio D. Di Claudio, Raffaele Parisi
2001 Signal Processing  
In this paper a novel neural network for digital equalization is introduced and described.  ...  Since transmitted symbols belong to a discrete alphabet, symbol demodulation can be e ectively recasted as a classiÿcation problem in the space of received symbols.  ...  Acknowledgements This work was supported in part by the Italian Ministry for University and Technological and Scientiÿc Research (M.U.R.S.T.).  ... 
doi:10.1016/s0165-1684(01)00129-3 fatcat:3uyh47k2jve3rl5lk4qotgt3vu

Distributed recursive learning for shape recognition through multiscale trees

Luca Lombardi, Alfredo Petrosino
2007 Image and Vision Computing  
by symbolic processing. q  ...  The proposed neural network model is able to facilitate the exchange of information between symbolic and sub-symbolic domains and deal with structured organization of information, that is typically required  ...  Acknowledgements We thank Marco Maggini from University of Siena for having provided BPTS software. We also thank the anonymous referees for the comments and remarks received.  ... 
doi:10.1016/j.imavis.2006.01.022 fatcat:7ydnfjz7y5darbysjlmygwugqa

Burst-by-burst adaptive turbo-coded radial basis function-assisted decision feedback equalization

M.S. Yee, T.H. Liew, L. Hanzo
2001 IEEE Transactions on Communications  
The performance of the proposed radial basis function (RBF) assisted turbo-coded adaptive modulation scheme is characterized in a wideband channel scenario.  ...  We commence by introducing the novel concept of the Jacobian RBF equalizer, which is a reduced-complexity version of the conventional RBF equalizer.  ...  Index Terms-Adaptive modulation, AQAM, decision feedback equalizer, DFE, Jacobian logarithm, neural network, radial basis function, RBF, turbo coding. I.  ... 
doi:10.1109/26.966070 fatcat:ooz2dmxkfbcgphvibqjramn4ri

Communication Channel Equalization-Pattern Recognition or Neural Networks?

Satnam Singh, Wayne Blanding, Vishal Ravindra, Krishna Pattipati
2006 2006 International Conference on Communication Technology  
The communication channel equalization is a difficult problem, especially when the channel is nonlinear and complex.  ...  In this paper, a comparison is made among the latest neural network techniques (Complex Minimal Resource Allocation Networks (CMRAN) [1]), a classical communication technique (Viterbi algorithm), and two  ...  Some of the examples are multilayer feedforward networks, radial basis function (RBF) networks, and recurrent neural networks.  ... 
doi:10.1109/icct.2006.341737 fatcat:ia33mzttbrcgjkpvj5jdct7dse

Communication Channel Equalization- Pattern Recognition or Neural Networks?

Satnam Singh, Wayne Blanding, Vishal Ravindra, Krishna Pattipati
2006 2006 International Conference on Communication Technology  
The communication channel equalization is a difficult problem, especially when the channel is nonlinear and complex.  ...  In this paper, a comparison is made among the latest neural network techniques (Complex Minimal Resource Allocation Networks (CMRAN) [1]), a classical communication technique (Viterbi algorithm), and two  ...  Some of the examples are multilayer feedforward networks, radial basis function (RBF) networks, and recurrent neural networks.  ... 
doi:10.1109/icct.2006.342046 fatcat:mrjkasw53vet7nado43ogwix54

Channel Equalization Using Dynamic Fuzzy Neural Networks

Ming-Bin Li, Meng Joo Er
2008 IFAC Proceedings Volumes  
In this paper, a dynamic fuzzy neural network (DFNN) is applying for communication channel equalization problem.  ...  The simulation results show that DFNN equalizer is superior to other equalizers such as recurrent neural network (RNN) and minimal resource allocation networks (MRAN) in terms of bit error rate (BER).  ...  (MLP), Radial Basis Function (RBF) networks and Recurrent Neural Networks (RNN) (Chen et al. (1990 (Chen et al. ( , 1991 (Chen et al. ( , 1993 ; Kechriotis et al. (1994) ; Kumar et al. (2000) ; Li  ... 
doi:10.3182/20080706-5-kr-1001.00685 fatcat:lpkssmtjwvhuboqwxdipoyqbpa

Analyzing Echo-state Networks Using Fractal Dimension [article]

Norbert Michael Mayer, Oliver Obst
2022 arXiv   pre-print
This connection between fractal dimension and network connectivity is an interesting new direction for recurrent neural network initialization and reservoir computing.  ...  We show potential usage of this fractal dimension with regard to optimization of recurrent neural network initialization.  ...  We thank Bo Ruei Jiang, Gorri Anil Kumar, and Ming Jie Li for their help. We thank Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) for their financial support.  ... 
arXiv:2205.09348v2 fatcat:4uixp4khjvdcdl6a3bpxc2sepa

Nonlinear Channel Equalization With Gaussian Processes for Regression

F. Perez-Cruz, J.J. Murillo-Fuentes, S. Caro
2008 IEEE Transactions on Signal Processing  
GPR's main advantage, compared to previous nonlinear estimation approaches, lies on their capability to optimize the kernel hyperparameters by maximum likelihood, which improves its performance significantly  ...  We propose Gaussian processes for regression as a novel nonlinear equalizer for digital communications receivers.  ...  Several nonlinear detection procedures have been proposed to address this problem with varying degrees of success, such as multi-layered perceptrons (MLPs) [2] , radial basis function networks (RBFNs)  ... 
doi:10.1109/tsp.2008.928512 fatcat:kn6clafpvrebngidmclm6oeium
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