Implementation Analog Neural Network for Electronic Nose using Field Programable Analog Arrays (FPAA)

Abstract

Electronic nose is a device detecting odors which is designed to resemble the ability of the human nose, usually applied to the robot. The process of identification of the electronic nose will run into a problem when the gas which is detected has the same chemical element. Misidentification due to the similarity of chemical properties of gases is possible; it can be solved using neural network algorithms. The attendance of Field Programmable Analog Array (FPAA) enables the design and implementation of an analog neural network, while the advantage of analog neural network which is an input signal from the sensor can be processed directly by the FPAA without having to be converted into a digital signal. Direct analog signal process can reduce errors due to conversion and speed up the computing process. The small size and low power usage of FPAA are very suitable when it is used for the implementation of the electronic nose that will be applied to the robot. From this study, it was shown that the implementation of analog neural network in FPAA can support the performance of electronic nose in terms of flexibility (resource component required), speed, and power consumption. To build an analog neural network with three input nodes and two output nodes only need two pieces of Configurable Analog Block (CAB), of the four provided by the FPAA. Analog neural network construction has a speed of the process 0.375 μs, and requires only 59 ± 18mW resources. DOI: http://dx.doi.org/10.11591/ijece.v2i6.1501