A Tea Classification Method Based on an Olfactory System Model

González, Eduardo; Li, Guang; Ruiz, Yusely; Zhang, Jin

doi:10.1007/978-1-4020-8387-7_129

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…They are useful in understanding the signal processing mechanism of olfactory systems. Therefore many mathematical models of olfactory systems have been applied to pattern recognition, often with remarkable results, which help to understand the olfactory information processing (Liljenström and Wu, 1995;Kozma et al, 2003;Gonzalez et al, 2007;Li G. et al, 2007;Li X. et al, 2006;Ma and Krings, 2009;Wu et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A biologically inspired model for pattern recognition

González

Liljenström

Ruiz

et al. 2010

J. Zhejiang Univ. Sci. B

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Abstract:In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.

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Section: Introductionmentioning

confidence: 99%

A biologically inspired model for pattern recognition

González

Liljenström

Ruiz

et al. 2010

J. Zhejiang Univ. Sci. B

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“…Electronic nose has been successfully used for tea classification and grading [2,5,6,12]. In general, an electronic nose is a smart instrument that is designed to detect and discriminate among complex odours using an array of sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Different data analysis and machine learning methods have been tried to analyse and classify the signals from the sensor array of the electronic nose system. For example, these signals were processed using Principal Components Analysis (PCA), Fuzzy C Means algorithm (FCM), Self-Organizing Map (SOM) method along with a Radial Basis Function network (RBF), Probabilistic Neural Network classifier, Back-Propagation network and Olfactory model [3,5,6,8,11,12]. Good performance has been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Application of Conformal Predictors to Tea Classification Based on Electronic Nose

Nouretdinov

Gammerman

et al. 2010

IFIP Advances in Information and Communication Technology

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International audienceIn this paper, we present an investigation into the performance of conformal predictors for discriminating the aroma of different types of tea using an electronic nose system based on gas sensors. We propose a new non-conformity measure for the implementation of conformal predictors based on Support Vector Machine for multi-class classification problems. The experimental results have shown the good performance of the implemented conformal predictors

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A Bionic Model Inspired on the Olfactory System

González

Ruiz

2013

V Latin American Congress on Biomedical Engineering CLAIB 2011 May 16-21, 2011, Habana, Cuba

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A Tea Classification Method Based on an Olfactory System Model

Cited by 6 publications

References 9 publications

A biologically inspired model for pattern recognition

A biologically inspired model for pattern recognition

Application of Conformal Predictors to Tea Classification Based on Electronic Nose

A Bionic Model Inspired on the Olfactory System

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