Ths signal processing technqiue of FET type electrolyte sensors using the back propagation neural network was studied to reduce the interference effects of the different electrolytes. The FET-type electrolyte sensors, pH-ISFET, K-ISFET, and Ca-ISFET, were prepared to measure the pH, K, and Ca electr...
Ths signal processing technqiue of FET type electrolyte sensors using the back propagation neural network was studied to reduce the interference effects of the different electrolytes. The FET-type electrolyte sensors, pH-ISFET, K-ISFET, and Ca-ISFET, were prepared to measure the pH, K, and Ca electrolytes. Neural network consisted of three layers was learned with 8 patterns and 9 patterns. The sensor output obtained with arbitrary concentrations was processed by the learned neural network. The errors obtained from calibration curve for pH, K, and Ca were .+-.0.039 pH, .+-.2.508 mmol/l, and .+-.1.807 mmol/l, respectively, without considering the interference effects. The errors of the network output for pH, K, and Ca were reduced to .+-.0.005 pH, .+-.0.436 mmol/l, and .+-.0.381 mmol/l in case of 9 patterns, respectively. the signal processing using the neural network can reduce the errors ofthe electrolyte sensor outputs caused by the interference effect, thereby providing effectiveness in the improvement of the sensor selectivity.
Ths signal processing technqiue of FET type electrolyte sensors using the back propagation neural network was studied to reduce the interference effects of the different electrolytes. The FET-type electrolyte sensors, pH-ISFET, K-ISFET, and Ca-ISFET, were prepared to measure the pH, K, and Ca electrolytes. Neural network consisted of three layers was learned with 8 patterns and 9 patterns. The sensor output obtained with arbitrary concentrations was processed by the learned neural network. The errors obtained from calibration curve for pH, K, and Ca were .+-.0.039 pH, .+-.2.508 mmol/l, and .+-.1.807 mmol/l, respectively, without considering the interference effects. The errors of the network output for pH, K, and Ca were reduced to .+-.0.005 pH, .+-.0.436 mmol/l, and .+-.0.381 mmol/l in case of 9 patterns, respectively. the signal processing using the neural network can reduce the errors ofthe electrolyte sensor outputs caused by the interference effect, thereby providing effectiveness in the improvement of the sensor selectivity.
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