An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures by Bin Li, Xuewen Rong, Yibin Li

“…Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. …”
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Hybrid Sensor Placement Framework Using Criterion-Guided Candidate Selection and Optimization by Se-Hee Kim, JungHyun Kyung, Jae-Hyoung An, Hee-Chang Eun

“…These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. …”
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Predicting the Traffic Crashes of Taxi Drivers by Applying the Non-Linear Learning of ANFIS-PSO with M5 Model Tree by E. Abbasi, M. Hadji Hosseinlou

“…A novel technique is been developed by applying nonlinear-learning of composition model of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization (PSO) with M5 model tree. In order to improve the generalization ability of a single data driving algorithm, a cluster of ANFIS models with different nodes and hidden layers are implemented to extract the inherent relationship between traffic accident rates and human factors. …”
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