Fuzzy clustering neural networks for real-time odor recognition system

6Citations
Citations of this article
22Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

The aim of this study is to develop a novel fuzzy clustering neural network(FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network. Copyright © 2007 B. Karlik and K. Yüksek.

Cite

CITATION STYLE

APA

Karlik, B., & Yüksek, K. (2007). Fuzzy clustering neural networks for real-time odor recognition system. Journal of Automated Methods and Management in Chemistry, 2007. https://doi.org/10.1155/2007/38405

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free