Artificial neural network based soft estimator for estimation of transducer static nonlinearity

International Journal of Neural Systems
Amar Partap SinghShakti Kumar

Abstract

In this work, the development of an Artificial Neural Network (ANN) based soft estimator is reported for the estimation of static-nonlinearity associated with the transducers. Under the realm of ANN based transducer modeling, only two neural models have been suggested to estimate the static-nonlinearity associated with the transducers with quite successful results. The first existing model is based on the concept of a functional link artificial neural network (FLANN) trained with mu-LMS (Least Mean Squares) learning algorithm. The second one is based on the architecture of a single layer linear ANN trained with alpha-LMS learning algorithm. However, both these models suffer from the problem of slow convergence (learning). In order to circumvent this problem, it is proposed to synthesize the direct model of transducers using the concept of a Polynomial-ANN (polynomial artificial neural network) trained with Levenberg-Marquardt (LM) learning algorithm. The proposed Polynomial-ANN oriented transducer model is implemented based on the topology of a single-layer feed-forward back-propagation-ANN. The proposed neural modeling technique provided an extremely fast convergence speed with increased accuracy for the estimation of transduc...Continue Reading

References

May 1, 1997·Journal of Biomedical Materials Research·D L WheelerJ O Hollinger
Feb 20, 2002·International Journal of Neural Systems·Gary Yen, Haiming Lu
Feb 5, 2008·IEEE Transactions on Neural Networks·M Torii, M T Hagan
Feb 7, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·J C PatraG Panda
Feb 7, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·J C PatraG Panda
Jan 1, 1994·IEEE Transactions on Neural Networks·M T Hagan, M B Menhaj

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Citations

Oct 26, 2013·International Journal of Neural Systems·Alex Alexandridis

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