An Embedded Simplified Fuzzy ARTMAP Implemented on a Microcontroller for Food Classification
| Autor: | N. Laguarda-Miró, Eduardo Garcia-Breijo, Luis Gil Sánchez, Jose Garrigues |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Microcontroller
Conductometry Computer science Honey classification honey classification neural networks fuzzy ARTMAP microcontroller Fuzzy ARTMAP lcsh:Chemical technology Biochemistry Fuzzy logic INGENIERIA QUIMICA Article Analytical Chemistry Pattern Recognition Automated TECNOLOGIA ELECTRONICA Computer graphics User-Computer Interface Fuzzy Logic Microcomputers Food classification Miniaturization Computer Graphics lcsh:TP1-1185 Electrical and Electronic Engineering MATLAB Instrumentation computer.programming_language Graphical user interface Artificial neural network business.industry Equipment Design Honey Atomic and Molecular Physics and Optics Equipment Failure Analysis Pattern recognition (psychology) business computer Neural networks Computer hardware Algorithms Food Analysis |
| Zdroj: | Sensors, Vol 13, Iss 8, Pp 10418-10429 (2013) Sensors (Basel, Switzerland) Sensors; Volume 13; Issue 8; Pages: 10418-10429 RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname Scopus-Elsevier |
| ISSN: | 1424-8220 |
| Popis: | In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food The financial support from the Spanish Government (MAT2012-38429-C04-04, FEDER funds) is gratefully acknowledged. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|