Parameter-Less Simulated Kalman Filter

Nor Hidayati, Abdul Aziz and Zuwairie, Ibrahim and Nor Azlina, Ab. Aziz and Saifudin, Razali (2017) Parameter-Less Simulated Kalman Filter. International Journal of Software Engineering & Computer Sciences (IJSECS), 3. pp. 129-137. ISSN 2289-8522. (Published)

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Simulated Kalman Filter (SKF) algorithm is a new population-based metaheuristic optimization algorithm. In the original SKF algorithm, three parameter values are assigned during initialization, the initial error covariance, P(0), the process noise, Q, and the measurement noise, R. Further studies on the effect of P(0), Q and R values suggest that the SKF algorithm can be realized as a parameter-less algorithm. Instead of using constant values suggested for the parameters, this study uses random values for all three parameters, P(0), Q and R. Experimental results show that the parameter-less SKF managed to converge to near-optimal solution and performs as good as the original SKF algorithm.

Item Type: Article
Uncontrolled Keywords: Optimization, Simulated Kalman Filter, Parameter-less
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Prof. Madya Dr. Zuwairi Ibrahim
Date Deposited: 14 Mar 2017 07:16
Last Modified: 08 Feb 2018 02:46
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