文章基本信息

标题：Neural Network for Electronic Nose using Field Programmable Analog Arrays
其他标题：Neural Network for Electronic Nose using Field Programmable Analog Arrays
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作者：Helmy Widyantara ; Muhammad Rivai ; Djoko Purwanto 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2012
卷号：2
期号：6
页码：739-747
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Electronic nose is a device detecting odors which is designed to resemblethe ability of the human nose, usually applied to the robot. The process ofidentification of the electronic nose will run into a problem when the gaswhich is detected has the same chemical element. Misidentification due tothe similarity of chemical properties of gases is possible; it can be solvedusing neural network algorithms. The attendance of Field ProgrammableAnalog Array (FPAA) enables the design and implementation of ananalog neural network, while the advantage of analog neural networkwhich is an input signal from the sensor can be processed directly by theFPAA without having to be converted into a digital signal. Direct analogsignal process can reduce errors due to conversion and speed up thecomputing process. The small size and low power usage of FPAA are verysuitable when it is used for the implementation of the electronic nose thatwill be applied to the robot. From this study, it was shown that theimplementation of analog neural network in FPAA can support theperformance of electronic nose in terms of flexibility (resource componentrequired), speed, and power consumption. To build an analog neuralnetwork with three input nodes and two output nodes only need twopieces of Configurable Analog Block (CAB), of the four provided by theFPAA. Analog neural network construction has a speed of the process0.375 μs, and requires only 59 ± 18mW resources.DOI:http://dx.doi.org/10.11591/ijece.v2i6.1501
其他摘要：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
关键词：Instrumentation and Control;FPAA; Analog Neural Network; Electronic Nose