scholarly journals A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

2017 ◽  
pp. 1291
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Prediction Method ◽  
Aircraft Carrier ◽  
Swarm Optimization ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

scholarly journals Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Jianhua Liu ◽  
Linfan Liu ◽  
Jing He ◽  
Changfan Zhang ◽  
Kaihui Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
State Identification ◽  
Kernel Parameter ◽  
Heavy Haul ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

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