Artificial olfaction system with hardware on-chip learning neural networks

This paper presents a hardware implementation of a multilayer feed-forward neural network based on back-propagation. The implementation is assumed to design and implement modules that emulate FF-BP functions with computing blocks of the predefined System Generator library and user defined blocks integrated in the System Generator library. The main application of the developed structure is an artificial olfactory system used to recognize the type of coffee presented in a test chamber. Data acquisition was achieved through the PC-MIO-16E-1 acquisition card and a virtual instrument, developed in Labview, for signal pre-processing and data logging into text files. The patterns presented (the type of coffee) have been recognized through neural networks. In order to select the RNA with the highest accuracy in recognising the coffee type, several different RNAs were simulated.