Modeling of optoelectronic devices through neuro-fuzzy architectures
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The advantages offered by the electronic component LED (Light Emitting Diode) have caused a quick and wide application of this device in replacement of incandescent lights. However, in its combined application, the relationship between the design variables and the desired effect or result is very complex and it becomes difficult to model by conventional techniques. This work consists of the development of a technique, through comparative analysis of neuro-fuzzy architectures, to make possible to obtain the luminous intensity values of brake lights using LEDs from design data.
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Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), v. 3070, p. 1175-1180.
