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Journal of Zhejiang University SCIENCE B 2010 Vol.11 No.2 P.115-126


A biologically inspired model for pattern recognition

Author(s):  Eduardo GONZALEZ, Hans LILJENSTRÖ,M, Yusely RUIZ, Guang LI

Affiliation(s):  Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   guangli@zju.edu.cn

Key Words:  Olfactory system, Neural network, Bionic model, Pattern recognition

Eduardo GONZALEZ, Hans LILJENSTRÖM, Yusely RUIZ, Guang LI. A biologically inspired model for pattern recognition[J]. Journal of Zhejiang University Science B, 2010, 11(2): 115-126.

@article{title="A biologically inspired model for pattern recognition",
author="Eduardo GONZALEZ, Hans LILJENSTRÖM, Yusely RUIZ, Guang LI",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A biologically inspired model for pattern recognition
%A M
%A Yusely RUIZ
%A Guang LI
%J Journal of Zhejiang University SCIENCE B
%V 11
%N 2
%P 115-126
%@ 1673-1581
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0910427

T1 - A biologically inspired model for pattern recognition
A1 - Eduardo GONZALEZ
A1 - M
A1 - Yusely RUIZ
A1 - Guang LI
J0 - Journal of Zhejiang University Science B
VL - 11
IS - 2
SP - 115
EP - 126
%@ 1673-1581
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0910427

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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