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Abstract
Wireless sensor networks have been utilized to monitor complex manufacturing processes but missing data from sensors cause problems for data-based applications. In this paper, a missing data estimation algorithm, GS-MPSO-WKNN (Gaussian mutation and simulated annealing-based memetic particle swarm optimization for weighted K nearest neighbours), based on a weighted K nearest neighbour (WKNN) and memetic computing is proposed. The GS-MPSO developed in our previous work is adopted in order to adjust the feature weights for the WKNN. A real world data set from a semiconductor manufacturing process is used to evaluate GS-MPSO-WKNN. Experimental results show that GS-MPSO-WKNN can reach a higher estimation accuracy, and GS-MPSO-WKNN is also robust to a high missing data ratio.
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