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RISS 인기검색어
- 검색키워드
- 저자 : Mehdi Karrari (검색결과 2 건)
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Salman Ahmadi,Mehdi Karrari 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.1
In this paper, a new multi-model approach is proposed for identification of nonlinear systems. In similar identification methods, the operating space is partitioned and a local model is suggested for each partition. In such approaches, since the same linear structure is often used for all local models; huge number of local linear models is usually required to reasonably model an operating region with severely nonlinear dynamics. Therefore the size of the global model may exponentially increase; and as a result model robustness may decrease. In the proposed approach the best model structure is selected for the particular nonlinear study system in an iterative approach. At each iteration, a choice is made to increase number of local models and/or increase the local model complexity. Furthermore, it determines the complexity of local models based on increasing the model accuracy and ensuring the model robustness. In order to optimize the model approximation capability and model robustness, a model term selection approach based on a forward orthogonal least squares algorithm and a criterion that minimizes the sum of the variance of the parameter estimates is applied. Simulation results show that the proposed method results in an excellent validation performance with fewer parameters.
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Distributed Incremental EM Algorithm for Density Estimation in Peer-to-Peer Networks
Behrooz Safarinejadian,Mohammad B. Menhaj,Mehdi Karrari 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In this paper a distributed incremental EM algorithm (DIEM) is proposed for density estimation and clustering of data distributed over the nodes of a network. Environmental modeling using a sensor network or clustering a data set in a computer network are some of the applications of this algorithm. After a full derivation of the distributed EM algorithm, it will be shown that DIEM converges faster than the standard distributed EM (DEM) algorithm. In DIEM, the data set of each node is partitioned into disjoint blocks of data which incrementally partial E-steps are performed over. Simulation results show that DIEM remarkably outperforms DEM.
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