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Data-driven soft sensors have been widely used in process systems for delivering online estimations of hard-to-measure yet important quality-related variables. However, in many data-driven soft sensor applications, the process may be strongly nonlinear, and the number of labeled samples is limited, which are two major difficulties in developing high-accuracy soft sensors. To cope with this issue,...
How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical...
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