Simulation and Optimization of Artificial Neural Network Based Air Quality Estimator

Abstract

E-sensor which are generally based on concept of E-nose are specially made to distinguish odours .In the present research work. E-sensor is developed using artificial intelligence technique to identify the concentration of carbon monoxide in a polluted environment. Data record access using Metal oxide sensor. The available data is broken into the number of segments .The length of data segment and the neurons in hidden layer is varied in number to find the optimized model of artificial neural network model using Mat Lab Coding. The artificial neural network model is optimized by verification in terms of mean squared error and regression. The regression is verified for training ,testing, validation and all. The mean squared error and regression are the artificial neural network model performance parameter.