A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Machine learning of radial basis function neural network based on Kalman filter: Implementation
In this paper we test three new sequential machine learning algorithms for radial basis function (RBF) neural network based on Kalman filter theory. Three new algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. Having introduced and derived mathematical model of each algorithm in the previous part of the paper, in this part we test and assess their performance using standard test sets from machine learning community. RBF neural network and three developed algorithms are implemented in MATLAB® programming environment. Experimental results obtained on real data sets as well as on real engineering problem show that developed algorithms result in more accurate models of the problem being investigated based on radial basis function neural network.
Machine learning of radial basis function neural network based on Kalman filter: Implementation
In this paper we test three new sequential machine learning algorithms for radial basis function (RBF) neural network based on Kalman filter theory. Three new algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. Having introduced and derived mathematical model of each algorithm in the previous part of the paper, in this part we test and assess their performance using standard test sets from machine learning community. RBF neural network and three developed algorithms are implemented in MATLAB® programming environment. Experimental results obtained on real data sets as well as on real engineering problem show that developed algorithms result in more accurate models of the problem being investigated based on radial basis function neural network.
Machine learning of radial basis function neural network based on Kalman filter: Implementation
Vuković Najdan L. (author) / Miljković Zoran Đ. (author)
2014
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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