Cart (Loading....) | Create Account
Close category search window
 

An Interval Type-2 Neural Fuzzy Chip With On-Chip Incremental Learning Ability for Time-Varying Data Sequence Prediction and System Control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chia-Feng Juang ; Dept. of Electr. Eng., Nat. Chung-Hsing Univ., Taichung, Taiwan ; Chi-You Chen

This paper proposes a new circuit to implement a Mamdani-type interval type-2 neural fuzzy chip with on-chip incremental learning ability (IT2NFC-OL) for applications in changing environments. Traditional interval type-2 fuzzy systems use an iterative procedure to find the system outputs, which is computationally expensive, especially for hardware implementation. To address this problem, the IT2NFC-OL uses a simplified type reduction operation to reduce the hardware implementation cost without degrading the learning performance. The software-implemented IT2NFC-OL is characterized by online structure learning and parameter learning using a gradient descent algorithm. The learned fuzzy model is then implemented in a field-programmable gate array (FPGA) chip. The FPGA-implemented IT2NFC-OL performs not only fuzzy inference but also online consequent parameter learning for applications in changing environments. Novel circuits for the computation of system outputs and the update of interval consequent values are proposed. The learning performance of the software-implemented IT2NFC-OL and the on-chip learning ability are verified with applications to time-varying data sequence prediction and system control problems and by comparisons with different software-implemented type-1 and type-2 neural fuzzy systems and interval type-2 fuzzy chips.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:25 ,  Issue: 1 )

Date of Publication:

Jan. 2014

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.