Optimization for Deep Takagi-Sugeno-Kang Fuzzy Classifier By Self-Adaptive Hybrid Search Evolutionary Algorithm with Competitive Behavior-Reference-Cited by-同舟云学术

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Optimization for Deep Takagi-Sugeno-Kang Fuzzy Classifier By Self-Adaptive Hybrid Search Evolutionary Algorithm with Competitive Behavior

Published:2024-06-30 Issue: Volume:97 Page:1-9
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Feng Xiao¹,Zheng Zhiwen¹,Yu Yongbin¹,Wang Xiangxiang¹,Wang Jingya¹,Han Xinyi¹,Zhang Ziyue¹,Cai Jingye¹,Wen Shiping²

Affiliation:

1. University of Electronic Science and Technology of China,School of Information and Software Engineering,Chengdu,China,610054

2. The University of Technology,Australian AI Institute, Faculty of Engineering and Information Technology,Sydney,NSW,Australia,2007

Funder

National Science and Technology Major Project

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650548.pdf?arnumber=10650548

Reference23 articles.

1. Novel Heterogeneous Mode-dependent Impulsive Synchronization for Piecewise T-S Fuzzy Probabilistic Coupled Delayed Neural Networks

2. Dynamic Pinning Synchronization of Fuzzy-dependent-switched Coupled Memristive Neural Networks with Mismatched Dimensions on Time Scales

3. Robust Adaptive Tracking Control for Hypersonic Vehicle Based on Interval Type-2 Fuzzy Logic System and Small-Gain Approach

4. Nonfragile consensus of nonlinear multiagent systems with intrinsic delays via aperiodic memory sampled‐data control

5. Adaptive Event-Triggered Fuzzy Positioning Control for Unmanned Marine Vehicles With Actuator Saturation and Hybrid Attacks

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