A Radial Basis Function Neural Network Classifier for the Discrimination of Individual Odor Using Responses of Thick-Film Tin-Oxide Sensors

Kumar, Ravi; Das, R. R.; Mishra, Vishnu Narayan; Dwivedi, R.

doi:10.1109/jsen.2009.2030072

Cited by 18 publications

(6 citation statements)

References 27 publications

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“…In this case, the sensing signals could be analyzed using the combined system of the five classifiers, multilayer perceptron (MLP), Gaussian mixture models (GMM), radial basis function (RBF), K-nearest neighbors (KNN), and probabilistic principal component analysis (PPCA), and the individual classifiers [ 109 ]. For odor discrimination, a thick-film tin-oxide sensor array was fabricated and employed in a neural network algorithm for analysis and classification [ 110 ]. This also means that a portable electronic nose based on a micro-resistive sensor array could be easy to implement and verify.…”

Section: Chemiresistive Sensorsmentioning

confidence: 99%

Towards a Chemiresistive Sensor-Integrated Electronic Nose: A Review

Chiu

Tang

2013

Sensors

199

133

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Electronic noses have potential applications in daily life, but are restricted by their bulky size and high price. This review focuses on the use of chemiresistive gas sensors, metal-oxide semiconductor gas sensors and conductive polymer gas sensors in an electronic nose for system integration to reduce size and cost. The review covers the system design considerations and the complementary metal-oxide-semiconductor integrated technology for a chemiresistive gas sensor electronic nose, including the integrated sensor array, its readout interface, and pattern recognition hardware. In addition, the state-of-the-art technology integrated in the electronic nose is also presented, such as the sensing front-end chip, electronic nose signal processing chip, and the electronic nose system-on-chip.

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Section: Chemiresistive Sensorsmentioning

confidence: 99%

Towards a Chemiresistive Sensor-Integrated Electronic Nose: A Review

Chiu

Tang

2013

Sensors

199

133

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“…The nodes of the input layer transfer input signals to the hidden layer. The transfer from the input layer space to the hidden layer is nonlinear, whereas that from the hidden layer to the output layer space is linear [ 24 , 25 ]. The output nodes in the network calculate a linear combination of basic functions given by the output layer.…”

Section: Structure Description Of the Rbfnn And The Kohonen Networkmentioning

confidence: 99%

Laser Gyro Temperature Compensation Using Modified RBFNN

Ding

Zhang

Huang

et al. 2014

Sensors

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To overcome the effect of temperature on laser gyro zero bias and to stabilize the laser gyro output, this study proposes a modified radial basis function neural network (RBFNN) based on a Kohonen network and an orthogonal least squares (OLS) algorithm. The modified method, which combines the pattern classification capability of the Kohonen network and the optimal choice capacity of OLS, avoids the random selection of RBFNN centers and improves the compensation accuracy of the RBFNN. It can quickly and accurately identify the effect of temperature on laser gyro zero bias. A number of comparable identification and compensation tests on a variety of temperature-changing situations are completed using the multiple linear regression (MLR), RBFNN and modified RBFNN methods. The test results based on several sets of gyro output in constant and changing temperature conditions demonstrate that the proposed method is able to overcome the effect of randomly selected RBFNN centers. The running time of the method is about 60 s shorter than that of traditional RBFNN under the same test conditions, which suggests that the calculations are reduced. Meanwhile, the compensated gyro output accuracy using the modified method is about 7.0 × 10−4 °/h; comparatively, the traditional RBFNN is about 9.0 × 10−4 °/h and the MLR is about 1.4 × 10−3 °/h.

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“…Kumar et al [ 15 ] developed a novel approach for odor discrimination of alcohols and alcoholic beverages using published data obtained from the responses of thick film tin oxide sensor array fabricated at our laboratory and employing a combination of transformed cluster analysis and radial basis function neural network. The performance of the new classifier was compared with others based on backpropagation (BP) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

Kumudha¹,

Venkatesan

2016

The Scientific World Journal

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Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

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A Radial Basis Function Neural Network Classifier for the Discrimination of Individual Odor Using Responses of Thick-Film Tin-Oxide Sensors

Cited by 18 publications

References 27 publications

Towards a Chemiresistive Sensor-Integrated Electronic Nose: A Review

Towards a Chemiresistive Sensor-Integrated Electronic Nose: A Review

Laser Gyro Temperature Compensation Using Modified RBFNN

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

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