Abstract
The possibility of artificial neural network usage for recognition of a signal of a multi-parameter sensor is described in this paper. The general structure of data acquisition channel with usage of neural networks as well as mathematical model of output signal of a multi-parameter sensor is studied in this article. The model of neural network, training algorithm and achieved results of simulation modeling of a multi-parameter sensor signal recognition using MATLAB software are presented at the end of this paper.
Highlights
Sensitivity to one measuring physical quantity was always one of the main requirements to sensors
It is necessary to use According to these features, the mathematical model of multiparameter sensors (MPS) output signal could be presented as multiplication of two different-order polynomials with different coefficients: simulator of the data acquisition channel MCh Sim for this purpose
Each of the vectors consists of two input values, which simulate MPS output signal and two output values, which simulate the values of input physical quantities
Summary
Sensitivity to one measuring physical quantity was always one of the main requirements to sensors. If the different parameters of MPS output signal depend on separate physical quantities, a problem with construction of appropriate data acquisition system does not arise. Such situation is not typical for MPS. The systems with MPS use dependences of output signal sensitivity on measured physical quantities in different operation modes for calculation of data acquisition results of different physical quantities. It is proposed below to consider the approach of artificial neural network usage for identification of separate physical quantities from single MPS signal in multi-sensor data acquisition system
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