%0 Journal Article %1 santhoshkv2013intelligent %A Santhosh K V, B K Roy %D 2013 %E ACEEE, %J ACEEE Int. J. on Electrical and Power Engineering %K Artificial Capacitive Network Neural Non Optimization Pressure Sensor Temperature compensation estimation linear modeling %N 1 %T An Intelligent Pressure Measurement Technique by Capacitance Pressure Sensor using Optimized ANN %U /brokenurl#doi.searchdl.org/01/IJEPE/4/1/1076.pdf %V 4 %X Design of an intelligent pressure measurement technique by Capacitance Pressure Sensor (CPS) using an optimized Artificial Neural Network (ANN) is reported in this paper. The objectives of the present work are: (i) to extend the linearity range of measurement to 100% of input range, (ii) make the measurement technique adaptive to variation in physical parameters of diaphragm in CPS like, elasticity modulus and thickness, permittivity of dielectric constant, and temperature, and (iii) to achieve objectives (i) and (ii) using an optimized neural network. A suitable optimal ANN is added, replacing the conventional calibration circuit, in cascade to data conversion unit. The proposed measurement technique is tested considering variations in physical parameters of CPS, and temperature. These parametric variations are considered within the specified ranges. Results show that the proposed intelligent technique has fulfilled the objectives.

@article{santhoshkv2013intelligent, abstract = {Design of an intelligent pressure measurement technique by Capacitance Pressure Sensor (CPS) using an optimized Artificial Neural Network (ANN) is reported in this paper. The objectives of the present work are: (i) to extend the linearity range of measurement to 100% of input range, (ii) make the measurement technique adaptive to variation in physical parameters of diaphragm in CPS like, elasticity modulus and thickness, permittivity of dielectric constant, and temperature, and (iii) to achieve objectives (i) and (ii) using an optimized neural network. A suitable optimal ANN is added, replacing the conventional calibration circuit, in cascade to data conversion unit. The proposed measurement technique is tested considering variations in physical parameters of CPS, and temperature. These parametric variations are considered within the specified ranges. Results show that the proposed intelligent technique has fulfilled the objectives. }, added-at = {2013-03-09T06:40:15.000+0100}, author = {Santhosh K V, B K Roy}, biburl = {https://www.bibsonomy.org/bibtex/25aa302db43cefa6b85c48b6828f311c0/ideseditor}, editor = {ACEEE}, interhash = {81095f7ce3ea8d2c2d7fdd354c1ca601}, intrahash = {5aa302db43cefa6b85c48b6828f311c0}, journal = {ACEEE Int. J. on Electrical and Power Engineering}, keywords = {Artificial Capacitive Network Neural Non Optimization Pressure Sensor Temperature compensation estimation linear modeling}, month = feb, number = 1, timestamp = {2013-03-09T06:40:15.000+0100}, title = {An Intelligent Pressure Measurement Technique by Capacitance Pressure Sensor using Optimized ANN }, url = {/brokenurl#doi.searchdl.org/01/IJEPE/4/1/1076.pdf}, volume = 4, year = 2013 }