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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1644 条记录，以下是1251-1260 订阅

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Coordinate Signal control in Trunk road based on Genetic BP Neural network

Coordinate Signal Control in Trunk road based on Genetic BP ...

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3rd International conference on Instrumentation & Measurement, Computer, Communication and control (IMCCC)

作者： Lv, Shao-jiao Li, Chun-gui Li, Zhe-ming Guangxi Univ Sci & Technol Dept Elect & Informat Engn Liuzhou Peoples R China Guangxi Univ Sci & Technol Dept Elect Informat & Control Engn Dept Comp Engn Liuzhou Peoples R China

ISBN: (纸本)9780769551227

To achieve a minimal time to through the urban traffic trunk road. However, the traditional analytical algorithm can not be applied in actual traffic widely. By combining the genetic BP neural network with the traditional analytical, A novel dynamic two-direction green wave coordinate control strategy was proposed, which applied in solving the function optimization problem. Finally, the new algorithm is compared with the existing algorithms for simulation experiments, The experiment results shows that the method on convergence and optimization results were improved.

关键词： coordinate signal control the traditional analytical algorithm Genetic BP Neural network The delayed time

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IIR Filter Design and Implementation for 2D Active Absorbing Wavemaker System 3

IIR Filter Design and Implementation for 2D Active Absorbing...

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3rd International conference on Computer Science and network Technology (ICCSNT)

作者： Yang, Hongqi Li, Muguo Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116023 Peoples R China

ISBN: (纸本)9781479905614

Wavemakers are essential facilities for physical model tests in coastal engineering field. The working principle of a wavemaker system is generating desired waves by controlling the mechanical motion of wave paddle. The absorbing control system is realized by an infinite impulse response (IIR) digital filter that approximates the transfer function in frequency domain. This paper presents the design and implementation of the BR filer for a piston-type absorbing wavemaker system in a 2D wave flume. Based on first-order wavemaker theory, a frequency domain absorption transfer function is modeled. Then an BR filter is obtained by approximating the transfer function with non-linear least square optimization. In practical systems, time delay exists in the control loop. Therefore, a delay compensation term to the transfer function is proposed. Experimental results on a real system show that the system performs well for both regular and irregular waves.

关键词： wavemaker active absorption IIR filter time delay control system

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Fault Tolerant Virtual network Embedding Algorithm based on Redundant Backup Resource

Fault Tolerant Virtual Network Embedding Algorithm based on ...

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3rd International conference on Instrumentation & Measurement, Computer, Communication and control (IMCCC)

作者： Yuan, Ying Wang, Cuirong Wang, Cong Zhang, Chongyang Zhu, Na Northeastern Univ Informat Sci & Engn Shenyang Peoples R China Northeastern Univ Qinhuangdao Qinhuangdao Peoples R China Northeastern Univ Qinhuangdao Sch Comp Ctr Qinhuangdao Peoples R China Sch Qinhuangdao Inst Technol Qinhuangdao Peoples R China

ISBN: (纸本)9780769551227

network virtualization allows multiple virtual networks to coexist on a shared physical substrate infrastructure. As network virtualization becomes popular, the problem of efficiently mapping a virtual network while guaranteeing its survivability in the event of failures becomes increasingly important. In this paper, the reliability problem of virtual network is solved based on a node and link redundant backup strategy. We model the virtual network mapping problem as an integer linear programming and present a discrete particle swarm optimization based algorithm to solve the problem. Experimental results show that the proposed algorithm has higher recovery success ratio and can reduce backup bandwidth simultaneously.

关键词： network virtualization virtual network mapping reliable mapping

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Application of the improved BP neural network in optimization design of control system of simple pendulum surface plate

Application of the improved BP neural network in optimizatio...

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2013 3rd International conference on Advanced Measurement and Test, AMT 2013

作者： Zhang, Hong Zhou Wang, Wei College of Mechanic and Electrical Engineering Tarim University Alaer Xinjiang 843300 China

ISBN: (纸本)9783037857168

According to the characteristics of large angle of pendulum movement, in order to ensure the stability of the fixed plate motion pendulum, to analysis quantitatively the relation of oscillation angle and plate movement, it designed an automatic flat control system with STC89C52RC as the core of control chip, collected the information of oscillation angle by the sensor of angle,controls the angle of surface plate by advanced four phase stepper motor,uses the improved BP neural network to real-time correction in the software design part, the control system has high control precision, the stability and speedability of control system of surface plate were improved by measuring. © (2013) Trans Tech Publications, Switzerland.

关键词： Pendulums

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Estimation of the parameters of wavelet neural networks using simultaneous use of genetic algorithm and recursive least square

Estimation of the parameters of wavelet neural networks usin...

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2013 3rd IEEE International conference on Computer, control and Communication, IC4 2013

作者： Rezaie, Nooshin Khanesar, Mojtaba Ahmadieh Teshnehlab, Mohammad Computer Department of Islamic Azad University Science and Research Branch Tehran Iran Department of Electrical and Control Engineering Semnan University Semnan Iran Department of Control Engineering K.N. Toosi University of Tech. Tehran Iran

ISBN: (纸本)9781467358859

In this paper, a novel identification scheme based on wavelet neural network structure is proposed. The objective function for identification considered in this paper is the sum of squared error. In order to optimize this objective, the genetic algorithm (GA) which is a global optimization is used for the parameters which appear nonlinearly in the wavelet structure. Recursive least square algorithm is used for the parameters which appear linearly in the output of wavelet neural network because it is known to be an optimal estimator for these parameters. The proposed training algorithm is used to identify chaotic system and a highly nonlinear dynamical system. Simulation results show that the proposed method identifies input/output data with higher performance in terms of sum of squared error when it is compared to gradient descent method. © 2013 IEEE.

关键词： Genetic algorithms

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3rd International conference on control, Instrumentation, and Automation, ICCIA 2013

3rd International Conference on Control, Instrumentation, an...

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3rd International conference on control, Instrumentation, and Automation, ICCIA 2013

ISBN: (纸本)9781479931170

The proceedings contain 64 papers. The topics discussed include: adaptive control of a two axis gimbal system;time delay robust AQM for Internet congestion control;optimal PID tuning based on Krill Herd optimization algorithm;bilateral control of teleoperation systems via robust PID controllers based on LMI;formulation of transparency in bilateral teleoperation systems: a robust approach;a new adaptive sliding-based congestion control strategy for a large scale TCP/IP network with differentiated services;robust stabilization of uncertain non-homogeneous Markov jump linear systems;fuzzy optimized control based on T-S model for satellite attitude control system to reduce limit cycle;observer-based robust model predictive control of a class of nonlinear systems with both state and input delays;the design of robust fractional order PI controller for liquid level control of a spherical tank;transient performance improvement using composite nonlinear feedback and integral sliding surface for matched and unmatched uncertain MIMO linear systems;observer-based direct adaptive neural control for a class of nonlinear non-affine systems with unknown control direction;and comparison of H-inf and GIPC control methods for attitude control of rigid spacecrafts.

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A Nonlinear Optimal Iterative Learning control Algorithm Based on RBF Neural network and Clonal Selection Algorithm

A Nonlinear Optimal Iterative Learning Control Algorithm Bas...

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3rd International conference on Advanced Engineering Materials and Technology (AEMT 2013)

作者： Li, Hengjie Hao, Xiaohong Pei, Xiping Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9783037857649

Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration;another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.

关键词： Clonal Selection Algorithm RBF neural networks Iterative Learning control

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Application of stochastic control to analysis and optimization of TCP

Application of stochastic control to analysis and optimizati...

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3rd Australian control conference (AUCC)

作者： Miller, Alexander Miller, Boris Russian Acad Sci AA Kharkevich Inst Informat Transmiss Problems Moscow Russia

ISBN: (纸本)9781479924981;9781479924974

The article considers the optimal stochastic control approach to the analysis of TCP. Generally the analysis of TCP schemes is based on so-called fluid models which provide the asymptotic behavior of the queuing system where the number of jobs is huge. At this level of consideration only the asymptotic results could be obtained and the real performance of existing protocols is still unclear particularly if there are the seasonal changes and congestions. Meanwhile, the models of controllable Markov chains (CMC) are more appropriate to the analysis of control of flows in the Internet that has been observed by many authors long ago. The principal difficulty of the CMC application is the high dimension particularly for connected controllable Markov chains (CCMC). But nowadays this problem is less important due to the development of multiprocessor supercomputers that make the numerical solution of the optimal control problems for CMC more achievable. Models of CCMC arise in queuing systems with many service lines where some idle lines may be used to avoid the congestion if the principal lines have been subjected the huge workload. Here we suggest the tensor form of such CMC description and give the dynamic programming equation in corresponding tensor form. As examples we consider the results of CMC approach to control the system with one service line, system with two service lines, namely the main and reserve ones, having different service rates and the cost of service, and to the system with incomplete information about the router state.

关键词： dynamic programming queueing theory stochastic processes stochastic programming telecommunication network routing tensors transport protocols

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The Application of BP Neural network Learning Algorithm Based on the Particle Swarm optimization

The Application of BP Neural Network Learning Algorithm Base...

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3rd International conference on Mechatronics and Intelligent Materials (MIM 2013)

作者： Sun, Zhihong Wang, Jun Xu, Baoji Air Force Logist Coll Dept Fdn Math Staff Room Xuzhou Jiangsu Peoples R China Air Force Logist Coll Dept Bomb Xuzhou Jiangsu Peoples R China Air Force Logist Coll Dept Finance Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9783037857106

The development of real estate has been affected by various social factors, including economic factors. BP neural network can more accurately forecast the trend of real estate industry according to economic development indicators. But BP neural network is slow convergence in the training process, and easily falls into local optimum. The BP neural network learning algorithm based on the particle swarm optimization (PSO) optimizes the weights and thresholds of the network by PSO algorithm, then to train BP neural network. The experimental results show that the performance of this new algorithm is better than BP neural network, but also has good convergence.

关键词： BP neural network particle swarm optimization (PSO) real estate prediction model

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Temperature control Measures' Intelligent optimization for Quasi- Mass Concrete Structure

Temperature Control Measures' Intelligent Optimization for Q...

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3rd International conference on Civil Engineering, Architecture and Building Materials (CEABM 2013)

作者： Huang, Yaoying Ding, Yuemei Lv, Xiaoman Tang, Tengfei China Three Gorges Univ Coll Hydraul & Environm Engn Yichang Peoples R China

ISBN: (纸本)9783037857762

Related to temperature control measures and material parameters, crack prevention by temperature control in quasi-mass concrete is an optimization problem including multiple complicated factors. This study tries to do some optimization of temperature control measures in the given concrete thermodynamics parameters circumstance. The quasi-mass concrete structure's minimum value of the relationship between principal tensile stress duration curves of internal and surface and the tensile strength growth curve are taken as the input, and the gate pier surface heat preservation effect, pouring temperature, water cooling temperature, water cooling time are taken as the output, we establish the optimal temperature control measures by the neural network model. Applying the uniform design principle to combine the temperature control parameters, and using the finite element method to analyze the temperature field and creep stress field in the quasi-mass concrete structure containing cooling water pipes, we obtain the samples, for training the network, to describe the nonlinear relationship between the principal tension stress duration curve and the tension strength growth curve and the different temperature control measures. After inputting the fitting minimum value of relationship between the principal tension stress duration curve and the tension strength growth curve to the trained network, the system is able to automatically select the optimal temperature control measures for crack prevention. The example shows that the proposed optimal neural network model for temperature control measures is feasible.

关键词： Quasi-mass concrete Surface heat preservation Water cooling Pouring temperature Intelligent optimization

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