An improved fuzzy ARTMAP and Q-learning agent model for pattern classification

Farhad Pourpanah, Ran Wang, Chee Peng Lim, Xizhao Wang, Manjeevan Seera, Choo Jun Tan

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)

Abstract

The Fuzzy ARTMAP (FAM) network is an online supervised neural network that operates by computing the similarity level between the new sample and those prototype nodes stored in its network against a threshold. In our previous study, we have developed a multi-agent system consisting of an ensemble of FAM networks and Q-learning, known as QMACS, for data classification. In this paper, an Improved QMACS (IQMACS) model with trust measurement using a combination of Q-learning and Bayesian formalism is proposed. A number of benchmark and real-world problems, i.e., motor fault detection and human motion detection, are conducted to evaluate the effectiveness of IQMACS. Statistical features are extracted from real-world case studies and utilized for classification with IQMACS, QMACS, and their constituents. The experimental results indicate that IQMACS produces better classification performance by combining the outcomes of its constituents as compared with those of QMACS and other related methods.

Original languageEnglish
Pages (from-to)139-152
Number of pages14
JournalNeurocomputing
Volume359
DOIs
Publication statusPublished - 24 Sep 2019
Externally publishedYes

Keywords

  • Fuzzy ARTMAP
  • Human motion detection
  • Motor fault detection
  • Multi-agent system
  • Pattern classification
  • Q-learning

Cite this