Enhancing the linearity characteristics of photoelectric displacement sensor based on extreme learning machine method

Abstract

Photoelectric displacement sensors rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. If the sensor output is nonlinear, it will produce a whole assortment of problems. This paper presents a method to compensate the nonlinearity of the photoelectric displacement sensor based on the extreme learning machine (ELM) method which significantly reduces the amount of time needed to train a neural network with the output voltage of the optical displacement sensor and the measured input displacement to eliminate the nonlinear errors in the training process. The use of this proposed method was demonstrated through computer simulation with the experimental data of the sensor. The results revealed that the proposed method compensated the presence of nonlinearity in the sensor with very low training time, lowest mean squared error (MSE) value, and better linearity. This research work involved less computational complexity, and it behaved a good performance for nonlinearity compensation for the photoelectric displacement sensor and has a good application prospect.

Application of MZI Symmetrical Structure With Fiber Balls and Seven-Core Fiber in Microdisplacement Measurement

“…Microdisplacement sensors have wide applications in automatic equipment, robots, and road and bridge management [1][2][3][4][5][6]. High precision displacement sensors include the inductive displacement sensor [2], capacitive displacement sensor [3], photoelectric displacement sensor [4,5], ultrasonic displacement sensor [6], and so on. With the development of optical fiber technology, many optical fiber displacement sensors have emerged.…”