Multi-input and multi-output modeling method based on T-S fuzzy neural network and its application

2021 ◽  
Author(s):  
Haixu Ding ◽  
Jian Tang ◽  
Junfei Qiao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Modeling Method ◽  
Fuzzy Neural

scholarly journals A Bearing Performance Degradation Modeling Method Based on EMD-SVD and Fuzzy Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jingbo Gai ◽  
Yifan Hu ◽  
Junxian Shen
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Degradation Process ◽  
Test Sample ◽  
Performance Degradation ◽  
Modeling Method ◽  
Intrinsic Mode Functions ◽  
Degradation Modeling ◽  
Fuzzy Neural ◽  
Whole Life Cycle

Bearing performance degradation assessment has great significance to condition-based maintenance (CBM). A novel degradation modeling method based on EMD-SVD and fuzzy neural network (FNN) was proposed to identify and evaluate the degradation process of bearings in the whole life cycle accurately. Firstly, the vibration signals of bearings in known states were decomposed by empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMFs) containing feature information. Then, the selected key IMFs which contain the main features were decomposed by singular value decomposition (SVD). And the decomposed results were used as the training samples of FNN. At last, the output results of the tested data were normalized to the health index (HI) through learning and training of FNN, and then the performance degradation degree could be described by the distance between the test sample and the normal one. According to the case study, this modeling method could evaluate the performance degradation of bearings effectively and identify the early fault features accurately. This method also provided an important maintenance strategy for the CBM of bearings.

Modeling of Switched Reluctance Motor Based on Dynamic Fuzzy Neural Network

2012 ◽  
Vol 220-223 ◽  
pp. 665-668 ◽  
Author(s):  
Ai De Xu ◽  
Shan Shan Zhang ◽  
Di Sun
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Switched Reluctance Motor ◽  
Mathematic Model ◽  
Modeling Method ◽  
System Structure ◽  
Actual System ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Fuzzy Neural

This paper proposed a novel mathematic model for switched reluctance motor(SRM):dynamic fuzzy neural network(D-FNN) was used to model for SRM based on the inductance characteristics, namely experimentally measured sample data. Compared with other modeling method, the inductance based on D-FNN can be trained on line and has the advantages of compact system structure and strong generalization ability. The SRM system is simulated with the trained inductance model. Compared with the actual system, the current waves are similar. This proves the new modeling method is correct and feasible.

A Compensatory Fuzzy Neural Network Modeling Method Based on Particle Swarm Clustering

2011 ◽  
Vol 48-49 ◽  
pp. 5-8 ◽  
Author(s):  
Chun Tao Man ◽  
Tian Feng Wang ◽  
Xiao Bo Sun ◽  
Xin Xin Yang ◽  
Jia Cui
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Decomposition Reaction ◽  
Modeling Method ◽  
Neural Network Modeling ◽  
Initial Structure ◽  
Initial Network ◽  
Fuzzy Neural

According to modeling problem for complex systems, a compensatory fuzzy neural network (CFNN) modeling method based on particle swarm clustering is proposed: the particle swarm clustering is used to automatically separate the space of input-output data, obtain the numbers of inference rules of fuzzy model and find fuzzy rules. Based on the rules, we modified fuzzy reasoning process and established initial structure of compensatory fuzzy neural network. Then using adaptive rate algorithm optimized initial network parameters, which can obtain a faster training speed and more precision. Simulation results show that the proposed network has successfully modeled the oxidation decomposition reaction process.

scholarly journals Innovative Modeling Method in Technical Training of High Jumpers

2012 ◽  
Vol 19 (4) ◽  
pp. 253-255
Author(s):  
Ilya Y. Krivetskiy ◽  
Grigoriy I. Popov
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Video Recording ◽  
Individual Performance ◽  
Modeling Method ◽  
Technical Training ◽  
Fine Tuning ◽  
Interactive System ◽  
Kinematic Characteristics ◽  
Fuzzy Neural

AbstractIntroduction. This essay introduces an innovative high jump technique modeling method that uses a cascaded fuzzy neural network. An interactive system for the prediction of the success of a high jump has been designed based on this method and it allows the creation of an individual model for highly skilled athletes to control the jumper's technical training. Material and methods. The research material included a video recording of 92 high jumps and analysis by 48 kinematic characteristics. The result allowed the fine tuning of the cascaded fuzzy neural network model in order to analyse successful and failed jumps. Results and conclusions. We have developed the interactive system based on the analysis of kinematic characteristics of the high jump and this allows individual performance models to be tailored for elite athletes. With the help of this instrument, which takes into account the individual biomechanical features of an athlete's jumping style, we can analyze all stages of a jump in detail, improve the technique through the targeted correction of specific motions and achieve the optimal combination of kinematic values for the best possible result.

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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