scholarly journals Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jin Zhang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Detection Method ◽  
Automatic Detection ◽  
Convergence Time ◽  
Series Data ◽  
Table Tennis ◽  
Trajectory Prediction ◽  
Acceptable Error ◽  
Fuzzy Neural

In the system design of table tennis robot, the important influencing factors of automatic detection of technical and tactical indicators for table tennis are table tennis rotation state, trajectory, and rebound force. But the general prediction algorithm cannot process the time series data and give the corresponding rotation state. Therefore, this paper studies the automatic detection method of technical and tactical indicators for table tennis based on the trajectory prediction using the compensation fuzzy neural network. In this paper, the compensation fuzzy neural network algorithm which combines the compensation fuzzy algorithm and recurrent neural network is selected to construct the automatic detection of technical and tactical indicators for table tennis. The experimental results show that the convergence time of the compensation fuzzy neural network is shorter, the training time is shortened, and the prediction accuracy is improved. At the same time, in terms of performance testing, the model can accurately distinguish the influence of table tennis rotation state and rebound on table tennis motion estimation, so as to improve the accuracy of motion trajectory prediction. In addition, the accuracy of trajectory prediction will be improved with the increase of input data. When the number of data reaches 30, the trajectory prediction error is within the actual acceptable error range.

Research of Test Model for Eggshell Crack Detection

2013 ◽  
Vol 846-847 ◽  
pp. 655-658
Author(s):  
Jian Yang ◽  
Chun Yan Xia ◽  
He Pan ◽  
Ying Shi ◽  
Xiu Ying Li
Keyword(s):  
Neural Network ◽  
Crack Detection ◽  
Fuzzy Neural Network ◽  
Detection Method ◽  
Experimental Result ◽  
Test Model ◽  
Inference Rules ◽  
Characteristic Parameters ◽  
Fuzzy Neural ◽  
Set Up

In order to realize the precise identification of eggshell crack, we design eggshell cracks detection method based on image processing and fuzzy neural network. Firstly this method gets two pieces image of eggs and processes, and then counts number of the same gray pixel. Determine five characteristic parameters as the input of fuzzy neural network. Set up a fuzzy neural network. Its structure is 5-10-1. Eggshell cracks and noise in egg images were distinguished using automatically learning and inference rules of fuzzy neural network. Use 147 groups of parameters for training network and rest 58 sample for verifying. Experimental result shows that the model can meet actual testing requirements with fast, stable, high precision and good robustness, easy to implement. Its precision reached 94.55%.

scholarly journals A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yuxian Zhang ◽  
Song Li ◽  
Xiaoyi Qian ◽  
Jianhui Wang
Keyword(s):  
Neural Network ◽  
Quality Index ◽  
Euclidean Distance ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Convergence Time ◽  
Computation Complexity ◽  
Index Model ◽  
Fuzzy Neural

The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

A FUZZY NEURAL NETWORK FOR KNOWLEDGE LEARNING

1994 ◽  
Vol 05 (01) ◽  
pp. 13-22 ◽  
Author(s):  
H.C. FU ◽  
J.J. SHANN
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Convergence Time ◽  
Training Procedure ◽  
Rule Based System ◽  
Precise Knowledge ◽  
Fuzzy Neural ◽  
Fine Tune ◽  
Logic Rule

This paper presents a fuzzy neural network for learning the knowledge of a fuzzy logic rule-based system. The network contains five layers: an Input Layer, Membership-function Layer, AND Layer, OR Layer, and Defuzzification Layer. We propose a backpropagation-like learning algorithm to train this neural network to acquire the fuzzy rules and to fine-tune the knowledge on the parameters of AND and OR nodes. Compared with methods other than the gradient descent search, the proposed learning process acquires more precise knowledge. In addition, the functions of the AND and OR nodes in the network are formulated with the minimum or maximum operations, respectively. Therefore, the adjustments of the learnable weights (parameters) can be focused on the dominant terms related to the (minimum/maximum) operations. The convergence time for the proposed learning algorithm is much faster than that for conventional backpropagation algorithms. In summary, the learnable weights (parameters) of the network are adjusted very quickly to obtain precise knowledge. Simulation results show that in learning the truck backer-upper problem, our network completes the training procedure in only several dozen epochs with an error rate of less than 1%.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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