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Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network

International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 94-110页

作者： Junfei Qiao Gaitang Han Honggui Han Wei Chai Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijingChina

Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment ***/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)*** structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural ***,a stability analysis method is given to prove the convergence of the proposed ***-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other ***/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic *** RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant ***,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.

关键词： Information processing ability recurrent fuzzy neural network Rule adaptive Wastewater treatment

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Prediction of PM2.5 Concentration Based on recurrent fuzzy neural network

Prediction of PM2.5 Concentration Based on Recurrent Fuzzy N...

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第36届中国控制会议

作者： Shanshan Zhou Wenjing Li Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781538629185

The prediction of PM2.5 is difficult because the variation of PM2.5 concentration is a nonlinear dynamic ***,a recurrent fuzzy neural network prediction method is proposed to predict the PM2.5 concentration in this ***,the partial least squares（PLS） algorithm is used to select key input variables as a preprocessing ***,a recurrent fuzzy neural network model is established and the gradient descent algorithm with an adaptive learning rate is used to train the neural *** results show that the recurrent neural network has better prediction performance and higher interpretability than fuzzy neural network（FNN） and radial-basis function（RBF） feed forward neural network.

关键词： PM2.5 prediction recurrent fuzzy neural network PLS adaptive learning rate

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Data-driven Control of Autonomous Vehicle using recurrent fuzzy neural network Combined with PID Method

Data-driven Control of Autonomous Vehicle using Recurrent Fu...

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第37届中国控制会议

作者： Yu Han Qidan Zhu Yao Xiao College of Automation Harbin Engineering University

This paper proposes a data-driven control method of autonomous vehicle to deal with target-reaching and obstacle avoidance *** unknown environment and modeling error bring uncertainties to control system,performance of accurate controller is *** needs to be designed online according to online and off-line *** network possesses good nonlinear approximation and learning ***,a kind of recurrent fuzzy neural network is used and related learning method is designed to predict optimal control *** improve robustness of control system,online tuning PID method is designed to obtain control *** establishing constrained optimization strategy,suitable parameters of PID are got to guarantee optimization of control under *** results prove that controller proposed in this paper is effective.

关键词： Autonomous Vehicle recurrent fuzzy neural network PID Robust Optimal Control Data-driven Control

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The fuzzy neural network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1417-1424页

作者： Jing Cheng Li Chen School of Aerospace Engineering Tsinghua UniversityBeijing 100084China School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350116China

In this paper,the dynamic evolution for a dualarm space robot capturing a spacecraft is studied,the impact effect and the coordinated stabilization control problem for postimpact closed chain system are *** first,the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach,and the dynamic equations of a spacecraft are obtained by Newton-Euler *** on the results,with the process of integral and simplify,the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer *** closed chain system is formed in the post-impact *** chain constraint equations are obtained by the constraints of closed-loop geometry and *** the closed chain constraint equations,the composite system dynamic equations are ***,the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system *** order to overcome the effects of uncertain system inertial parameters,the recurrent fuzzy neural network is used to approximate the unknown part,the control method with H∞tracking *** to the Lyapunov theory,the global stability is ***,the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between *** last,numerical examples simulate the response of the collision,and the efficiency of the control scheme is verified by the simulation results.

关键词： Capturing operation calm motion control closed chain system dual-arm space robot recurrent fuzzy neural network H∞tracking characteristic

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Learn to Predict PM2.5 Concentration with Image Contrast-Sensitive Features

Learn to Predict PM2.5 Concentration with Image Contrast-Sen...

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第37届中国控制会议

作者： Zengzeng He Xudong Ye Ke Gu Junfei Qiao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Huludao Power Supply Corporation Liaoning Electric Power Co.Ltd

PM2.5, which severely affects human health, is one of the most important indices for air quality estimation. There have been limited studies on the simple, fast and cheap PM2.5 concentration prediction and thus this paper presents a model of PM2.5 prediction based on image contrast-sensitive features. Two types of features were extracted from the images and utilized to estimate the PM2.5 concentration. Then, we establish a recurrent fuzzy neural network model, the parameters of which are trained by using the gradient descent algorithm with an adaptive learning rate. Experiment results indicate that the recurrent neural network has better prediction performance than traditional radial basis function and fuzzy neural network.

关键词： PM2.5 prediction contrast-sensitive image features recurrent fuzzy neural network

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