Multistability of Fuzzy Neural Networks With Rectified Linear Units and State-Dependent Switching Rules

Ou, Shiqin; Guo, Zhenyuan; Wang, Jun

doi:10.1109/tfuzz.2022.3204895

Cited by 7 publications

(2 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mapping function can be expressed as: to enable arbitrary fitting capability [34]. Furthermore, we have opted to utilize the Sigmoid function [35] as the activation function for output layers and the rectified linear unit (ReLU) [36] activation function for hidden layers. Specifically, these functions can be expressed as:…”

Section: Deep Neural Network Detection Schemementioning

confidence: 99%

A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications

Cao,

Yao,

Sun

et al. 2024

IEEE Photonics J.

View full text Add to dashboard Cite

Conventional multiple input multiple output (MIMO) detection algorithms face challenges related to computational complexity and limited performance when handling high-dimensional inputs and complex channel conditions. In order to enhance signal recovery accuracy in atmospheric turbulence channels for faster-than-Nyquist (FTN) optical wireless communication (OWC) systems, a deep learning (DL) based MIMO detector is proposed. By leveraging a deep neural network (DNN), it becomes possible to learn nonlinear mappings within MIMO systems, resulting in improved detection performance while reducing computational overheads. Simulation results validate that our proposed DNN detector achieves comparable performance to the maximum likelihood (ML) method, while reducing complexity by 40%.

show abstract

Section: Deep Neural Network Detection Schemementioning

confidence: 99%

A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications

Cao,

Yao,

Sun

et al. 2024

IEEE Photonics J.

View full text Add to dashboard Cite

show abstract

“…So far, many studies have been done on using fuzzy-neural systems in research tasks. [8][9][10][11][12] In past decades, some researchers have applied intelligent concepts such as neural networks, fuzzy systems, particle swarm optimization (PSO), GA, and hybrid systems to flight control to increase the controller's capability to adapt to different environments. [13][14][15][16][17] These articles show that intelligent control systems are better than conventional control systems.…”

Section: Introductionmentioning

confidence: 99%

Application of linguistic fuzzy neural network to landing control

Chien,

Juang

2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Most aircraft accidents occurred during the final approach. Wind disturbance is one of the significant factors in these accidents. During the landing phase, the Automatic Landing System (ALS) can help aircraft land safely and significantly reduce the pilot’s work loading. Control schemes of the conventional ALS usually use gain-scheduling and traditional PID control techniques. A traditional controller cannot control the aircraft if the weather conditions are beyond the allowed limits. To improve the performance of the landing control, this study applies a linguistic fuzzy neural network (LFNN) to replace the conventional controller of ALS. Adaptive learning rules are proposed to enhance the LFNN control ability. The method used to obtain adaptive learning rules is the Lyapunov stability theory. Moreover, the convergence of the system performance error is proved by the Lyapunov theory. This study also compares previously proposed control schemes in aircraft landing control. Different turbulence strengths are implemented into the flight simulation to make the proposed controller more robust and adaptive to various wind disturbance conditions. The LFNN controller can successfully overcome 75 ft/s wind speed, while the adaptive LFNN can reach 80 ft/s with optimal learning rates. Using optimal convergence theorems, the proposed controller performs better than the controllers trained by a fixed learning rate.

show abstract

Multistability of switched complex-valued neural networks with state-dependent switching rules

Guo

et al. 2023

Neurocomputing

View full text Add to dashboard Cite

Multistability of Fuzzy Neural Networks With Rectified Linear Units and State-Dependent Switching Rules

Cited by 7 publications

References 81 publications

A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications

A MIMO Detector With Deep-Neural-Network for Faster-Than-Nyquist Optical Wireless Communications

Application of linguistic fuzzy neural network to landing control

Multistability of switched complex-valued neural networks with state-dependent switching rules

Contact Info

Product

Resources

About