A Practically Validated Adaptive Calibration Technique using Optimized Artificial Neural Network for Level Measurement by Capacitance Level Sensor

Santhosh Kv, Binoy Krishna Roy

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Design of an adaptive calibration technique using an optimized artificial neural network for liquid-level measurement is discussed in this paper. The objective of the present work is to design and validate an adaptive calibration technique so as (1) to extend the linearity range of measurement to 100% of full-scale input range and (2) to make the measurement technique adaptive of variations in tank diameter, permittivity of liquid, liquid temperature, and to achieve objectives (1) and (2) using an optimized neural network. An optimized artificial neural network is a network having least possible number of hidden layers to achieve minimum mean square error between outputs and targets by comparing various algorithms, schemes, and transfer functions of neuron. The output of capacitance level sensor is capacitance. A data conversion unit is used to convert it to voltage. A suitable optimized artificial neural network is designed and used in place of conventional calibration circuit. The proposed technique is tested with simulated data and validated with practical data. Results show that proposed technique has fulfilled the set objectives.

Original languageEnglish
Pages (from-to)217-224
Number of pages8
JournalMeasurement and Control (United Kingdom)
Volume48
Issue number7
DOIs
Publication statusPublished - 01-09-2015

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Level measurement
Capacitance
Artificial Neural Network
Calibration
capacitance
Neural networks
Sensor
sensors
Sensors
Liquid
Liquids
Output
Minimum Mean Square Error
Measurement Techniques
Permittivity
liquid levels
Linearity
Mean square error
Range of data
Transfer Function

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Control and Optimization
  • Applied Mathematics

Cite this

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