Abstract
In recent years, gas-sensing devices have been widely used in various fields. Especially in the field of health, medicine is already a complete application, and electronic nose system is a system used in patients with symptom monitoring. Electronic nose system, he not only integrated the gas-sensing device, but also into the random probability model classifier, to face in the face of full noise or data drift when more stable processing of information as. In the previous literature, we can see that the electronic nose system can effectively distinguish the data through its own gas-like probability model classifier. With the increase in the number of sensors more and more complex, limited by the hardware restrictions and cannot further distinguish the effect. In order to be able to quickly correspond to and reduce the burden on the system, scalable adaptive probability model is a new concept. In order to be able to solve the same hardware in the framework of the rapid expansion of system resources, and at the same time enhance the ability to process data. In this paper, we discuss the application of the continuous restricted Boltzmann machine in the analysis and resolution of the signal after the electronic nose system. The paper analyzes the gas sensing data by using the computer software MATLAB, and proposes a multi - layer structure continuous restricted Boltzmann machine. Then the results of the analysis and the previous literature using digital circuits to achieve Continuous restricted Boltzmann machine. Then, two kinds of scalable digital continuous restricted Boltzmann machine are proposed to realize the scalable adaptive probability model of electronic nose system.