Machine Learning
Neural network design
Using MLP Networks to Classify Red Wines and Water Readings of an Electronic Tongue
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
Mosfet Modeling for Vlsi Simulation: Theory And Practice (International Series on Advances in Solid State Electronics)
Chemical Field Effect Transistor Response with Post Processing Supervised Neural Network
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Principles of Chemical Sensors
Principles of Chemical Sensors
| Hi-index | 0.00 |
The ion-sensitive field-effect transistor (ISFET) that is designed to detect a specific ionic activity is susceptible to interfering ions in mixed-ion environments causing the sensor to produce deceptive signals. The objective of this work is to improve the interpretation of ISFET signals in mixed-ion environments. The focus of the research is relating sensor signal to the targeted ion concentration by applying supervised neural network as post-processing stage as a method to overcome low selectivity issues. In this paper, we acquire ISFET voltage response data in potassium and ammonium mixed-ion solutions for the training of a multilayer perceptron with backpropagation algorithm. A constant-voltage constant-current readout interface circuit is applied to maintain constant bias of the sensor throughout the data collection process. Primary data from measured observations was fed to a feed-forward multilayer perceptron trained to classify levels of ionic concentrations in various levels of mixed-ion solutions. Accuracy of sensor response interpretation of ionic activity estimation is compared between with and without neural network post-processing stage. Neural network performance was also compared for voltage values with and without pre-processing voltage signals by referencing sensor response in deionized water. Further improvement of the network was approached by using an ensemble of similar structures of networks trained with backpropagation constructed using the bagging algorithm. Results show that neural network fed with dc voltage response from 4-sensor array is able to improve concentration estimation by 15% improvement compared to direct estimation based on a look-up table. Pre-processing the sensor response significantly improves the sensor signal repeatability correlation factor by 15.5% and reduces mean-square error by 98.3%, with a typical 20% improvement in output-target regression factor network performance. Averaging from ensemble system is shown to give a further 5% improvement on the output-target regression factor with consistently stable ion concentration estimations.