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Algorithms 2018, 11(11), 169; https://doi.org/10.3390/a11110169

Parameter Estimation of a Class of Neural Systems with Limit Cycles

Institute of System Engineering, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China
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Received: 11 September 2018 / Revised: 22 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
(This article belongs to the Special Issue Parameter Estimation Algorithms and Its Applications)
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Abstract

This work addresses parameter estimation of a class of neural systems with limit cycles. An identification model is formulated based on the discretized neural model. To estimate the parameter vector in the identification model, the recursive least-squares and stochastic gradient algorithms including their multi-innovation versions by introducing an innovation vector are proposed. The simulation results of the FitzHugh–Nagumo model indicate that the proposed algorithms perform according to the expected effectiveness. View Full-Text
Keywords: parameter estimation; neural system; recursive least-squares algorithm; stochastic gradient algorithm; innovation parameter estimation; neural system; recursive least-squares algorithm; stochastic gradient algorithm; innovation
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Lou, X.; Cai, X.; Cui, B. Parameter Estimation of a Class of Neural Systems with Limit Cycles. Algorithms 2018, 11, 169.

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