The Learning Algorithm for a Novel Fuzzy Neural Network

2006 ◽  
pp. 247-258
Author(s):  
Puyin Liu ◽  
Qiang Luo ◽  
Wenqiang Yang ◽  
Dongyun Yi
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural

Torque Control of Servo Load Simulator with Generalized Dynamic Fuzzy Neural Network Based on Grey Prediction

2011 ◽  
Vol 148-149 ◽  
pp. 707-712
Author(s):  
Li Wang ◽  
Lin Fang Qian ◽  
Qi Guo
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Torque Control ◽  
Grey Prediction ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Load Simulator ◽  
Load Simulation

Considering the testing requirements of dynamically loaded about servo system in weapons, a load simulator is presented in this paper and the transfer function of “extraneous torque" is obtained. In order to curb the amplitude of extra torque and achieve dynamic load simulation, this paper proposes a grey prediction-based fuzzy neural network controller, which selects Generalized Dynamic Fuzzy Neural Network as the learning algorithm and selects the ε-completeness as a criterion to determine the width of Gaussian functions. Simulation and experimental results show that the proposed torque controller can significantly reduce the amplitude of the extra torque.

Fuzzy Neural Network Hybrid Learning Control on AUV

2012 ◽  
Vol 468-471 ◽  
pp. 1732-1735
Author(s):  
Jing Zhao ◽  
Zhao Lin Han ◽  
Yuan Yuan Fang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Network Control ◽  
Learning Ability ◽  
B Spline ◽  
Hybrid Learning Algorithm ◽  
Fuzzy Neural

A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

Dynamic Controller Based on Ying Learning Algorithm for Medical Temperature System

2011 ◽  
Vol 187 ◽  
pp. 371-376
Author(s):  
Ping Zhang ◽  
Xiao Hong Hao ◽  
Heng Jie Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
The Novel ◽  
Fuzzy Neural ◽  
Learning Set ◽  
And Training ◽  
Novel Algorithm

In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. Ying Learning Dynamic Fuzzy Neural Network (YL-DFNN) algorithm is proposed. The Learning Set based on K-VNN is constituted from message. Then the framework of is designed and its stability is proved. Finally, Simulation indicates that the novel algorithm is fast, compact, and capable in generalization.

Fuzzy Neural Network Compensator for Gantry Stage Synchronous Motion

2011 ◽  
Vol 145 ◽  
pp. 234-239 ◽  
Author(s):  
Chin Sheng Chen ◽  
Mu Han Lee
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Tracking Error ◽  
Servo Systems ◽  
Synchronous Motion ◽  
Fuzzy Neural ◽  
Synchronous Error ◽  
Online Learning Algorithm

In this paper, a fuzzy neural network (FNN) compensator is proposed for the synchronous motion control of a gantry position stage. Firstly, the cascade control strategy is applied to reduce the single axis position tracking error. However, the synchronous error between dual servo systems is inevitable due to their inequality in characteristics and the environmental uncertainties. The FNN compensator and an online learning algorithm perform a fuzzy reasoning with two inputs of synchronous position and velocity errors between dual drive servo systems and generate the compensated force; the compensated force is fed back to the controller of each axis. The online learning algorithm adjusts the connected weighting of the neural network by using a supervised gradient descent methods, such that the define error function can be minimized. Finally, two kinds of position commands with high and low frequency are designed for the experiments, and the experimental results show that the proposed FNN compensator is feasible to improve the synchronous error of gantry stage.

A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

2019 ◽  
Vol 36 (4) ◽  
pp. 3263-3269 ◽  
Author(s):  
Chunmei He ◽  
Yaqi Liu ◽  
Tong Yao ◽  
Fanhua Xu ◽  
Yanyun Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fast Learning ◽  
Fuzzy Neural ◽  
Learning Machine
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