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Related Experiment Video

Updated: Jun 17, 2026

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

eFSM--a novel online neural-fuzzy semantic memory model.

Whye Loon Tung¹, Chai Quek

¹Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore. wltung@pmail.ntu.edu.sg

IEEE Transactions on Neural Networks

|December 17, 2009

Summary

This study introduces the evolving neural-fuzzy semantic memory (eFSM), a novel Mamdani-type neural-fuzzy system that incrementally learns and adapts its rule base from data. The eFSM model evolves its fuzzy rules for improved performance in dynamic environments.

Related Experiment Videos

Last Updated: Jun 17, 2026

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

Area of Science:

Artificial Intelligence
Machine Learning
Fuzzy Systems

Background:

Traditional fuzzy rule-based systems (FRBSs) often require manual rule base construction and lack adaptability.
Existing hybrid systems like neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs) address some limitations but may not fully handle nonstationary environments.
Evolving Mamdani-type NFSs with incremental learning are less explored compared to evolving Takagi-Sugeno (T-S) types.

Purpose of the Study:

To present the evolving neural-fuzzy semantic memory (eFSM) model, a novel Mamdani-type neural-fuzzy architecture.
To enable data-driven, incremental learning and adaptation of the fuzzy rule base.
To address the need for adaptive fuzzy systems in complex, dynamic, and nonstationary environments.

Main Methods:

Development of the evolving neural-fuzzy semantic memory (eFSM) model.
Implementation of incremental learning for the Mamdani-type fuzzy rule base.
Proposal of a novel parameter learning approach for fuzzy set tuning.
Dynamic rule base management: construction of new rules and pruning of obsolete ones.

Main Results:

The eFSM model successfully elicits interpretable Mamdani-type if-then fuzzy rules from numeric data.
The system incrementally learns and adapts its rule base with each new data sample.
The proposed model demonstrates the ability to maintain a current and compact set of fuzzy rules.
Evaluation on benchmark applications shows encouraging learning and modeling performance.

Conclusions:

The eFSM model provides an effective approach for adaptive fuzzy rule-based systems using incremental learning.
It enhances interpretability by extracting semantic knowledge as Mamdani fuzzy rules.
The model's ability to evolve its rule base makes it suitable for nonstationary environments.