The nonlinear relation between methane concentration and the
output voltage of the sensor is indicated by analysis of detection principle of
catalytic methane sensor.
This paper proposes a nonlinear correction model based on
functional link neural network (FLNN) with the output voltage of methane sensor
as input and the methane concentration as output to eliminate the nonlinear
errors in methane detection. By adding some high-order terms, the model applies
the single-layer network to realize the network supervised learning.
The approach has advantages of nonlinear approach ability
and independent on accurate mathematical model, it can improve network learning
speed and simplify the network structure.
The experimental result shows that the maximum relative
error of simulation curves is reduced to 0.86%, which is much smaller than that
of piecewise linear fitting curve with 3.09%. The detection accuracy of methane
sensor is improved.
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