Abstract
Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should 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 or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks.
Highlights
An electronic nose (e-nose) is an intelligent 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
The electronic nose developed in this research consists of a sensor array in which each sensor gives a different electrical response for a particular target vapor introduced into the sensing chamber
Pattern recognition techniques based on the principal component analysis and the Cerebellar Model Articulation Controller (CMAC) neural network model have been developed for learning different chemical odor vapors
Summary
An electronic nose (e-nose) is an intelligent 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. The CMAC is an algorithm that quantizes and generalizes input, produces active memory addresses, and produces an output by summing all the weights in the active. The state space detectors are often called the CMAC’s virtual memory This transformation contains quantization process and input generalization with generalization factor (width) [15]. If the output vectors of the CMAC do not match a desired output for a given input state, the weights pointed to by the physical addresses are updated using the following least mean square (LMS). An increase in the number of quantization levels, qimax, results in higher input resolution (Figure 3), but concurrently increases the size of the virtual address space, and slowing speed [21,22]. The CMAC has been used to solve various robotic problems, and applied in the field of controls, medical science, pattern recognition, signal processing and image processing [23,24]
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