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第28届中国控制与决策会议

作者： Qiang Lv Yang Zhang Huican Lin Academy of Armored Force Engineering

ISBN: (纸本)9781467397155

A recursive network is proposed by introducing memory neurons based on the RBF *** to the current output value of the network is related to the past input value in the network,the network will be able to identify the dynamics of the system without the need of explicitly feedback of input and output in the ***,the network is able to identify a system has an unknown order or an unknown *** algorithm and the theoretical rationality are provided in this *** validity of the method is verified by simulation of dynamic system identification,and it is of great significance in the field of adaptive control.

关键词： RBF Network System Identification Recursive Network gradient descent algorithm

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Local brightness adaptive image colour enhancement with Wasserstein distance

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IET IMAGE PROCESSING 2015年第1期9卷 43-53页

作者： Wang, Liqian Xiao, Liang Liu, Hongyi Wei, Zhihui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Jiangsu Prov Key LAB Spectral Imaging & Intellige Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China

Colour image enhancement is an important preprocessing phase of many image analysis tasks such as image segmentation, pattern recognition and so on. This study presents a new local brightness adaptive variational model using Wasserstein distance for colour image enhancement. Under the perceptually inspired variational framework, the proposed energy functional consists of an improved contrast energy term and a Wasserstein dispersion energy term. To better adjust image dynamic range, the authors propose a local brightness adaptive contrast energy term using the average brightness of image local patch as the local brightness indicator. To restore image true colours, a Wasserstein distance-based dispersion energy term is used to measure the statistical similarity between the original image and the enhanced image. The proposed energy functional is minimised by using a gradient descent algorithm. Two objective measures are used to quantitatively measure the enhancement quality. Experimental results demonstrate the efficiency of the proposed model for removing colour cast and haze, enhancing contrast, recovering details and equalising low key images.

关键词： image colour analysis image enhancement image segmentation gradient methods local brightness adaptive image colour enhancement Wasserstein distance image analysis image segmentation pattern recognition energy functional contrast energy Wasserstein dispersion energy image dynamic range image local patch gradient descent algorithm

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Efficient active control of fluid borne pulsation in hydraulic piping systems

Efficient active control of fluid borne pulsation in hydraul...

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2016 IEEE/CSAA International Conference on Aircraft Utility Systems(AUS)

作者： Huiwei Zheng Zongxia Jiao Yuanzhi Xu Lingyun Chai School of Automation Science and Electrical Engineering Beihang University

With the development of the hydraulic system of higher pressure and power,the fluid borne pulsation in pipelines becomes extremely harmful to the whole ***,the control of fluid borne pulsation is very significant for improving the performance of practical hydraulic *** traditional active control methods of fluid borne pulsation were found to be effective,while the existing weakness is model-based with huge computational consumption and poor robustness.A novel active control method is proposed here,using efficient gradient descent algorithm for parametric optimization.A fast servo valve is utilized as the actuator in the bypass fluid *** numerical and experimental verification are carried *** than 70%attenuation of pressure peaks can be obtained in the simulation,and experiments on a practical hydraulic system achieve about 35%pulsation *** performance on computation efficiency is also good compared with conventional methods,which means the present control method is effective and efficient.

关键词： Fluid borne pulsation active control method gradient descent algorithm piping system

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Unmixing Of Hyperpectral Image Using Nonlinear Parametric Model

Unmixing Of Hyperpectral Image Using Nonlinear Parametric Mo...

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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Thacker Unnatiben Navinkumar S K Parmar EC Department GCET Gujarat India

ISBN: (纸本)9781509007752

Hyperspectral image contains hundreds of bands so it is spectrally overloaded and contains extent information to differentiate spectrally unique material. Hyperspectral data generally used to identify the presence of material in scene. Almost all the hyperspectral cameras have spatial resolution limit (>5m per pixel) due to that each pixel can be a mixture of several materials. The process ofunmixing is to unmixone of these mixed pixels. There are two models available to approximate mixing, (i) Linear Mixing Model (LMM) (ii) Nonlinear Mixing Model (NMM). Over a time, various approaches have been devised to address LMM and it's unmixing. In LMM, macrospectral mixtures are assumed. Nonlinear model comes under consideration due to microscopic mixing scale. In this paper, Generalized bilinear model is used which is nonlinear parametric model to get mixed data. Its accuracy depends on parametric form and parameter value chosen. It comes under convex optimization problem, so it can be solved using any optimization technique. gradient descent algorithm (GDA) is employed to solve this optimization problem. Advantage of GDA over other unmixing techniques is that it transforms nonlinear model into linear one. To improve unmixing result, it is indeed advisable to consider spatial correlation among abundances. A novel approach has been introduced in this paper which considers 2~(nd)order neighborhood correlation between abundances. Using our approach one can achieve better segmentation.

关键词： hyperspectral image linear mixing model nonlinear mixing model unmixing generalized bilinear model gradient descent algorithm spatial correlation

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Experimental testing of a random neural network smart controller using a single zone test chamber

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IET NETWORKS 2015年第6期4卷 350-358页

作者： Javed, Abbas Larijani, Hadi Ahmadinia, Ali Emmanuel, Rohinton Gibson, Des Clark, Caspar Glasgow Caledonian Univ Sch Engn & Built Environm Glasgow Lanark Scotland Gas Sensing Solut Ltd Glasgow G68 9HQ Lanark Scotland Univ West Scotland Scottish Univ Phys Alliance Inst Thin Films Sensors & Imaging Paisley PA12BE Renfrew Scotland

Monitoring and analysis of energy use and indoor environmental conditions is an urgent need in large buildings to respond to changing conditions in an efficient manner. Correct estimation of occupancy will further improve energy performance. In this work, a smart controller for maintaining a comfortable environment using multiple random neural networks (RNNs) has been developed. The implementation of RNN-based controller is demonstrated to be more efficient on hardware and requires less memory compared to both artificial neural networks and model predictive controllers. This controller estimates the number of room occupants by using the information from wireless sensor nodes placed in the Heating, Ventilation and Air Conditioning (HVAC) duct and the room. For an occupied room, the controller can switch between thermal comfort mode (based on predicted mean vote set points) and user defined mode (i.e. occupant defined set points for heating/cooling/ventilation). Furthermore, the hybrid particle swarm optimisation with sequential quadratic programming training algorithms are used (for the first time to the best of the authors' knowledge) for training the RNN and results show that this algorithm outperforms the widely used gradient descent algorithm for RNN. The results show that occupancy estimation by smart controller is 83.08% accurate.

关键词： neurocontrollers intelligent control building management systems HVAC indoor environment ergonomics gradient methods quadratic programming particle swarm optimisation wireless sensor networks energy conservation occupancy estimation gradient descent algorithm sequential quadratic programming training algorithms hybrid particle swarm optimisation indoor environment predicted mean vote-based set points thermal comfort mode HVAC duct wireless sensor nodes heating-ventilation-air conditioning model predictive controllers artificial neural networks RNN-based controller comfortable environment building energy control system environmental conditions energy usage monitoring energy usage analysis single zone test chamber random neural network smart controller experimental testing

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A comparative study of neuro fuzzy and recurrent neuro fuzzy model-based controllers for real-time industrial processes

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2015年第1期3卷 412-426页

作者： Subathra, B. Seshadhri, S. Radhakrishnan, T. K. Kalasalingam Univ ICE Dept Madurai Tamil Nadu India Kalasalingam Univ Int Res Ctr Madurai Tamil Nadu India Natl Inst Technol Tiruchirappalli Tamil Nadu India

Nonlinearities in system dynamics and the multivariable nature of processes offer a stiff challenge in designing predictive controllers that improve process performance in industries. This investigation presents a recurrent neuro fuzzy network (RNFN) model for a nonlinear multivariable system in process industries and a methodology to design model-predictive controllers (MPCs) using the proposed model. The RNFN model combines the learning features of artificial neural networks with human cognition capabilities of fuzzy systems. Therefore, RNFN leads to a modelling framework that has the ability not only to learn the model parameters, but also makes decision on operating region of the nonlinear model depending on the input-output data. Furthermore, the recurrent structure and the introduction of a memory unit between the fuzzy inference and fuzzification layer enhance the prediction capability due to the use of past input-output data, making the model more suitable for designing predictive controllers. Next, the MPC design methodology that exploits the advantages of the RNFN model to optimize the control moves is presented. The proposed MPC uses the gradient descent algorithm to minimize the control moves as against the traditional state-space approaches that require complex computations and solvers. Therefore, implementing the proposed MPC in embedded hardware becomes easier. The proposed modelling framework and the MPC design methodology are illustrated using experiments on a laboratory-scale quadruple tank. Our experiments show that the proposed RNFN-based MPC performs better than the neuro fuzzy network-based MPC for both servo and regulatory responses.

关键词： modelling intelligent techniques hybrid soft computing recurrent neuro fuzzy network MPC gradient descent algorithm

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Optimal Synthesis of the Slider-crank Mechanism for Path Generation Based on the Generalized Kinematic Mapping of Constrained Plane Motions 14

Optimal Synthesis of the Slider-crank Mechanism for Path Gen...

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14th International-Federation-for-the-Promotion-of-Mechanism-and-Machine-Science World Congress

作者： Chen, Genliang Wang, Hao Yu, Weidong Zhao, Yong Shanghai Jiao Tong Univ Shanghai Key Lab Digital Mfg Thin Walled Struct State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

ISBN: (纸本)9789860460988

This paper presents a new strategy for the optimal synthesis of the planar slider-crank mechanism for continuous path generations. Based on the generalized kinematic mapping of constrained plane motions, the desired path for the coupler link is mapped into the three-dimensional projective space as a spatial surface, which is regarded as the target kinematic manifold. Also, the potential motions of the coupler link constrained by the crank and the slider are projected on the target manifold in the image space, whose difference can be regarded as an alternative description for the kinematic conflict of the mechanism with the design requirement. Thus, a metric for the structure error between the candidate mechanism and the ideal one for the desired path is set up in terms of the area of a ruled surface, the distance surface intercepted from the target manifold, which is also selected as the objective function for the optimal synthesis. To obtain the final optimum based on the initial design variables with high convergence rate, the gradient descent algorithm is introduced. A numerical example for the optimal synthesis of the planar slider-crank mechanism for an elliptic trajectory is studied to validate the effectiveness of the proposed approach.

关键词： Optimal synthesis Planar slider-crank mechanism Continuous path generations Generalized kinematic mapping gradient descent algorithm

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An adaptive fuzzy PI control for a class of unknown nonaffine systems 3

An adaptive fuzzy PI control for a class of unknown nonaffin...

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INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING & INFORMATION TECHNOLOGY (CEIT)

作者： Cherrat, Nidhal Boubertakh, Hamid Univ Jijel LAJ Jijel Algeria

ISBN: (纸本)9781479982134

In this work a fuzzy adaptive PI controller is proposed for a class of uncertain nonaffine nonlinear systems. The parameters of the PI controller that can achieve the control objectives are approximated by a set of fuzzy systems. The fuzzy systems' parameters are adjusted by the gradient descent algorithm. The purpose behind the use of this adaptation algorithm is to minimize the error between the unknown ideal PI controller and the fuzzy PI controller. The stability of the closed-loop system is proven analytically based on Lyapunov approach. A simulation example is presented to illustrate the performance of the proposed scheme.

关键词： Adaptive PI controller fuzzy systems nonaffine nonlinear systems gradient descent algorithm Lyapunov stability

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Micro-IMU-Based Motion Tracking System for Virtual Training 34

Micro-IMU-Based Motion Tracking System for Virtual Training

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34th Chinese Control Conference (CCC)

作者： Zhang, Yang Fei, Yunfeng Xu, Lin Sun, Guangyi Nankai Univ Inst Robot & Automat Informat Syst Tianjin Key Lab Intelligent Robot Tianjin Peoples R China Second Artillery Corps Engn Design & Res Inst Beijing Peoples R China

ISBN: (纸本)9789881563897

This paper presents the development of a low cost wireless real-time inertial body tracking system for virtual training. The system is designed to provide highly accurate human body motion capture and interactive three-dimensional (3-D) avatar steering, by combining low cost MEMS inertial measurement units (IMUs), wireless body sensor network (BSN), and Unity 3D virtual reality game engine. First, several wearable MEMS IMU sensors are placed on user's body and limbs according to human skeletal action, and each sensor performs a 9 degrees of freedom (DOF) tracking at a high-speed update rate. Second, a Zigbee-based BSN is designed to support up to 20 MEMS IMU sensor nodes data transmission at 50 Hz sampling frequency. All collected sensors' data are loaded to a Matlab-based PC program by means of serial port. In order to accurately estimate the local orientation of each IMU sensor, an optimized gradient descent algorithm is implemented. The algorithm uses a quaternion representation, which allows accelerometer and magnetometer data to be fused to compute the gyroscope measurement error as a quaternion derivative. Finally, the estimated orientation data by fusion algorithm are imported to a virtual environment, consisting of the 3-D virtual skeletal representation and the virtual scene for specific training. Experimental results indicate that the system achieves < 1 degrees static RMS error and < 2 degrees dynamic RMS error. The systems further expand the usability of low cost body tracking solution to virtual training in virtual environments.

关键词： micro inertial measurement unit (IMU) motion capture virtual training gradient descent algorithm wireless sensor network

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Fuzzy Model Control for Maglev Suspension System

Fuzzy Model Control for Maglev Suspension System

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International Conference on System Science and Engineering (ICSSE)

作者： Su, Kuo-Ho Duy-Thanh Pham Tsung, Tsing-Tshih Yang, Chan-Yun Chinese Culture Univ Grad Inst Digital Mechatron Technol 55 Hwa Kang Rd Taipei 11114 Taiwan Natl Taipei Univ Dept Elect Engn New Taipei Taiwan

ISBN: (纸本)9781614995227;9781614995210

The maglev suspension system or magnetic levitation system (MLS), using the electromagnetic force to float, can effectively reduce the mechanical vibration friction and wearing loss caused by contact operation. However the parameters in the mathematical model are related to the permanent magnet geometry, distance and the total mass of the platform. To achieve fast response, a fuzzy system and the parameter identification algorithm for maglev suspension system are proposed in the paper. The proposed first fuzzy model method is based on the table lookup scheme (TLS) to establish a common fuzzy system procedure. By gathering the input and output data pairs, a fuzzy system is created to replace the currently used mathematical model. On the other hand, due to the parameters are related to the hardware structure, the second gradient descent algorithm (GDA) is proposed. By using the gathered data pairs, the parameters of membership functions are updated through the gradient descent algorithm. In this paper, the fuzzy model systems based on TLS and GDA are applied to ensure the effectiveness of tracking performance by replacing the mathematical model of MLS. From the results, the chattering phenomenon is reduced and fast response can be obtained without degrading the tracking performance.

关键词： maglev suspension system fuzzy control fuzzy model system gradient descent algorithm

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