scholarly journals An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1317
Author(s):  
Xin Huang ◽  
Yuanping Zhu ◽  
Shuqin Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Human Action ◽  
Human Motion ◽  
Estimation Accuracy ◽  
Human Motion Analysis ◽  
Critical Feature ◽  
Fuzzy Neural ◽  
Motion Retrieval

Human motion retrieval and analysis is a useful means of activity recognition to 3D human bodies. An efficient method is proposed to estimate human motion by using symmetric joint points and limb features of various limb parts based on regression task. We primarily obtain the 3D coordinates of symmetric joint points based on the located waist and hip points. By introducing three critical feature points on torso and symmetric joint points’ matching on motion video sequences, the 3D coordinates of symmetric joint points and its asymmetric limb features will not be affected by shading and interference of limb on different postures. With the asymmetric limb features of various human parts, a dynamic regulated Fuzzy neural network (DRFNN) is proposed to estimate human motion for different asymmetric postures using learning algorithm of network parameters and weights. Finally, human sequential actions corresponding to different asymmetric postures are presented according to the best retrieval results by DRFNN based on 3D human action database. Experiments show that compared with the traditional adaptive self-organizing fuzzy neural network (SOFNN) model, the proposed algorithm has higher estimation accuracy and better presentation results compared with the existing human motion analysis algorithms.

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

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.

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