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DC Field | Value | Language |
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dc.contributor.author | Kapgate, Sachin N | - |
dc.contributor.author | Gupta, Saurav | - |
dc.contributor.author | Sahoo, Ajit Kumar | - |
dc.date.accessioned | 2018-03-08T11:40:03Z | - |
dc.date.available | 2018-03-08T11:40:03Z | - |
dc.date.issued | 2018-02 | - |
dc.identifier.citation | 5th International Conference on Signal Processing and Integrated Networks (SPIN 2018),Amity University, Noida, Uttar Pradesh, India, 22 - 23 February, 2018. | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/2932 | - |
dc.description | Copyright of this document belongs to proceedings publisher. | en_US |
dc.description.abstract | Many practical systems tend to have some extent of nonlinearity involved in their behavior. System identification and control design for nonlinear dynamical systems is achieving extensive attention in many practical applications. To model nonlinear system behavior, many mathematical models have been developed and employed in practical applications. This paper mainly focuses on application of least mean square (LMS) variants for the adaptive implementation of one such model known as nonlinear Volterra model. The challenging problems involved with the stability and convergence rate of traditional least mean square based approach are shown individually. The supporting analysis and simulations are provided to justify the efficacy of presented work. Least mean square variants based Volterra modeling approaches can be effectively applied in system control design, acoustic echo cancellation and stability analysis of the nonlinear systems. | en_US |
dc.subject | Signal processing | en_US |
dc.subject | Nonlinear System modeling | en_US |
dc.subject | Volterra model | en_US |
dc.subject | Least mean squares | en_US |
dc.title | Adaptive Volterra Modeling For Nonlinear Systems Based on LMS Variants | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2018_SPIN_SNKapgate_Adaptive.pdf | Conference Paper | 663.21 kB | Adobe PDF | View/Open |
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