Hazardous Odor Recognition by CMAC Based Neural Networks

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.

Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC

“…where is the excited memory address and is the ith segment address of the jth signal. For example, the quantization levels of input signals ( 1 , 2 , 3 ) are (3, 6, 8), respectively; the 1 mapping segment address is [ 11 , 12 , 13 , 14 ] = [5,6,3,4], the 2 mapping segment address is [ 21 , 22 , 23 , 24 ] = [9,6,7,8], and the 3 mapping segment address is [ 31 , 32 , 33 , 34 ] = [9,10,11,8]. According to the mode of the concatenation equation, the concatenation modes of the excited addresses are expressed as follows:…”

“…In 2009, Bucak and Karlık proposed the identification of harmful gases based on a CMAC neural network and the application of an electronic nose flu measuring volatile substances and identifying odors. The results showed that the recognition effect was up to at least 85% using this method [9]. In 2010, Bucak and Karlık used CMAC to help doctors identify liver diseases.…”

Inheritance based Intelligent Technique Employing Nested-XOR with Recursion for Recognition and Classification of Odors using Multi-Sensor Nose System

“…Scientists are researching the use of electronic noses to diagnose illness by smelling patients' breath with the possibility of installing tiny electronic noses in phone receivers, so that patients can simply breathe into the phone and wait for a diagnosis. High-tech snifters may be used not just for breath-smelling but also to detect other subtle changes in body odor that can indicate disease conditions (Wongchoosuk et al, 2009;Myrick et al, 2009;Tang et al, 2010;Jha and Yaava, 2011;Bucak and Karlık, 2009;Huang and Wan, 2009;Zeng et al, 2009;Lange et al, 2009;Vera et al, 2011;Song et al, 2011).…”

Quality Control of Olive Oils Using Machine Learning and Electronic Nose

“…E-nose is too beneficial for various applications as a food, cosmetic, and pharmaceutical with these features. Also e-nose is used in environmental discipline [10] or clinical diagnostics [11]. The use of electronic noses is described by many papers in food science and technology.…”

“…E-nose has array of sensors to detect and recognize different odors with low cost [9]. E-nose is too beneficial for various applications such as food, cosmetics, pharmaceuticals, and environmental disciplines [10]. E-nose is too beneficial for various applications as a food, cosmetic, and pharmaceutical with these features.…”