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This paper presents an improved particle swarm optimization (PSO) and discrete PSO (DPSO) with an enhancement operation by using a self-adaptive evolution strategies (ES). This improved PSO/DPSO is proposed for joint optimization of three-layer feedforward artificial neural network (ANN) structure and parameters (weights and bias), which is named ESPNet.(More)
This paper presents a new evolutionary artificial neural network (ANN) algorithm named IPSONet that is based on an improved particle swarm optimization (PSO). The improved PSO employs parameter automation strategy, velocity resetting, and crossover and mutations to significantly improve the performance of the original PSO algorithm in global search and(More)
Ensemble classification – combining the results of a set of base learners – has received much attention in the machine learning community and has demonstrated promising capabilities in improving classification accuracy. Compared with neural network or decision tree ensembles, there is no comprehensive empirical research in support vector machine (SVM)(More)
Manufacturing systems are subject to unscheduled downtime due to machine malfunction. A threshold or an alarm is usually obtained based on equipment lifetime distribution to trigger maintenance work orders. The gap between conventional method and industrial practice is that the threshold is fixed and does not consider the status change due to machine(More)
Flexible manufacturing systems (FMSs) are highly automated and require effective scheduling approaches to improve the system performance and integrate various control decision-making activities. In this paper, a multi-agent approach integrated with a filteredbeam-search (FBS)-based heuristic algorithm is proposed to study the dynamic scheduling problem in a(More)