Improving Static Characteristics of Infrared Sensor using Soft Computing Techniques

Abstract

This paper presents the design of an efficient soft computing technique for increasing the accuracy of the Infrared Sensor, by using the Support Vector Machines (SVM) and Neural Networks (NN). The analysis for SVM was done with different functions available and the results are presented. For Neural Networks, the analysis was done with different member functions and different number of. To determine the appropriate model, the model with minimum Root Mean square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when compared with the sensor's measurements were selected. The results indicate that the Neural Network model could reduce the relative RMSE by a factor of 0.58 and MAPE by a factor of 0.76 which is much greater than the results obtained from Support Vector Machine.