PENGARUH PRINCIPLE COMPONENT ANALYSIS TERHADAP TINGKAT IDENTIFIKASI NEURAL NETWORK PADA SISTEM SENSOR GAS - Muhammad Rivai (TELKOMNIKA (Telecommunication Computing Electronics and Control), 2007)
In recently, it has been developed a gas identification system consists of a semiconductor sensor array and Neural Network pattern recognition. In this study, it has been implemented a method of Principle Component Analysis (PCA) as a preprocessing of the Neural Network algorithm. The sensory array is composed of eight commercial semiconductor sensors. Three layer-Neural Network was trained with the back propagation technique within 5000 epochs. PCA could reduce the eight-dimension into three-dimension without any information losses.  The identification error rate was lower with the ratio of ~10-4 and the training period was shorter with the ratio of ~0.6. In generally, it can be concluded that the implementation of the PCA method into the Neural Network can enhance the performances of the neural include the identification rate and time consumed in the training phase.