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An electronic nose (e-nose) is an intelligent Ponatinib sensing device that uses an array of gas sensors of partial and overlapping selectivity, along with a pattern recognition component, to distinguish between both simple and complex odors. To date, e-noses have had a variety of use in a number of applications such as the food industry, medical diagnosis, Inhibitors,Modulators,Libraries mobile robots, environmental disasters or intelligent appliances [1�C10].The monitoring of the quality of air in an enclosed environment has always been an important concern. Hazardous odor (or gases) can be present as a result of leaks in tanks, piping, etc., and their presence needs to be monitored to prevent the accidental exposure to a hazardous condition.

Analytical chemistry instruments such as gas chromatographs (GC) and mass spectrometers (MS) have been used to analyze both hazardous and non-hazardous odors. GC and GC/MS systems can require a significant amount of human intervention to perform the analysis and then relate the Inhibitors,Modulators,Libraries analysis to something usable [4�C5]. The odor sensing system should Inhibitors,Modulators,Libraries be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network. In the last decades, the use of environmental monitoring has been rediscovered due to major advances in odor sensing technology and soft computing techniques such as artificial neural networks (ANN), fuzzy systems and the other artificial intelligence techniques [6�C10].

Nonlinear response characteristics and the use of an array of gas sensors have made artificial neural networks very attractive because of their capability to analyze multidimensional nonlinear sensor data, and to model sensor response, which is mathematically very difficult. Inhibitors,Modulators,Libraries In the past, work has been done on chemical gas sensors using Multilayer Perceptron (MLP) artificial neural networks. Gas sensor calibration is one of them [11]. However MLPs present a major disadvantage of slow training. This drawback makes them unsuitable for real time training and adaptive modeling. They require much iteration to converge and a large number of computations per iteration.In this paper, a fully operational CMAC based neural network recognition system which models the function of the biological nose is presented and applied to recognize hazardous odors.

One of the main advantages of CMAC based neural networks compared to MLP is their Brefeldin_A extremely fast learning capability. Different from MLPs, CMACs have simpler calculations, higher convergence speed and better generalization ability, and non-existing local minima. Therefore, they are widely applied in controls and real-time recognition problems [12�C13].The remaining selleck chem of the paper is arranged as follows. Section 2 briefly explains the basics of the CMAC neural network and its significant properties.