Gradient-Based Recursive Maximum Likelihood Identification of Jump Markov Non-Linear Systems [Elektronisk resurs]
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Braga, Andre R. (författare)
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20th International Conference on Information Fusion (Fusion)
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Fritsche, Carsten (författare)
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Gustafsson, Fredrik (författare)
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Bruno, Marcelo G. S. (författare)
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Linköpings universitet Institutionen för systemteknik (utgivare)
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Linköpings universitet Tekniska fakulteten (utgivare)
- Publicerad: IEEE, 2017
- Engelska.
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Ingår i: 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION). ; 228-234
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Sammanfattning
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- This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance.
Ämnesord
- Engineering and Technology (hsv)
- Electrical Engineering, Electronic Engineering, Information Engineering (hsv)
- Control Engineering (hsv)
- Teknik och teknologier (hsv)
- Elektroteknik och elektronik (hsv)
- Reglerteknik (hsv)
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