The Study of Membrane Fouling Modeling Method Based on Fuzzy Neural Network for Sewage Treatment Membrane Bioreactor

2007 ◽  
Author(s):  
Jingwen Tian ◽  
Meijuan Gao ◽  
Wenjiang Liao ◽  
Kai Li ◽  
and Dahang Zhang
Keyword(s):  
Neural Network ◽  
Membrane Fouling ◽  
Membrane Bioreactor ◽  
Fuzzy Neural Network ◽  
Sewage Treatment ◽  
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.

scholarly journals An intelligent detecting system for permeability prediction of MBR

2017 ◽  
Vol 77 (2) ◽  
pp. 467-478 ◽  
Author(s):  
Honggui Han ◽  
Shuo Zhang ◽  
Junfei Qiao ◽  
Xiaoshuang Wang
Keyword(s):  
Membrane Permeability ◽  
Membrane Fouling ◽  
Membrane Bioreactor ◽  
Fuzzy Neural Network ◽  
Least Squares Method ◽  
Wastewater Treatment Plants ◽  
Soft Computing Method ◽  
Fuzzy Neural ◽  
Partial Least Squares Method ◽  
Computing Method

Abstract The membrane bioreactor (MBR) has been widely used to purify wastewater in wastewater treatment plants. However, a critical difficulty of the MBR is membrane fouling. To reduce membrane fouling, in this work, an intelligent detecting system is developed to evaluate the performance of MBR by predicting the membrane permeability. This intelligent detecting system consists of two main parts. First, a soft computing method, based on the partial least squares method and the recurrent fuzzy neural network, is designed to find the nonlinear relations between the membrane permeability and the other variables. Second, a complete new platform connecting the sensors and the software is built, in order to enable the intelligent detecting system to handle complex algorithms. Finally, the simulation and experimental results demonstrate the reliability and effectiveness of the proposed intelligent detecting system, underlying the potential of this system for the online membrane permeability for detecting membrane fouling of MBR.

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.

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