Position and Speed Detection Method Based on Adaptive Extended Moving-Window Linear Regression for Traction Machine Drives

In this paper, a double locally weighted extreme learning machine model based on a moving window is developed to realize process modeling. To improve model performances, an improved sparrow-searching algorithm is proposed to optimize the parameters of the proposed model. The effectiveness of the proposed model and algorithm are verified by data from a hematite grinding process. The experimental results show that the proposed algorithm can significantly improve the global search ability and convergence speed in the parameter optimization of the proposed model. The proposed model can correctly estimate the grinding particle size which is expected to be applied to other complex industries.

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Position and Speed Detection Method Based on Adaptive Extended Moving-Window Linear Regression for Traction Machine Drives

Cited by 3 publications

References 33 publications

A New Rotor Position Estimation Method for Spherical Reluctance Motor Based on Inductance Characteristics Obtained From Measurement

A New Rotor Position Estimation Method for Spherical Reluctance Motor Based on Inductance Characteristics Obtained From Measurement

Adaptive Step-Size Predictive PLL Based Rotor Position Estimation Method for Sensorless IPMSM Drives

A Moving Window Double Locally Weighted Extreme Learning Machine on an Improved Sparrow Searching Algorithm and Its Case Study on a Hematite Grinding Process

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