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检索条件"任意字段=Conference on High Performance Algorithms and Software for Nonlinear Optimization"

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Multi-Objective optimization of Double-Folded Yagi Antenna Using Genetic algorithms

Multi-Objective Optimization of Double-Folded Yagi Antenna U...

引用

International conference on Computational Intelligence and software Engineering (CiSE)

作者： Bin Yuan Xiu Wang Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

The novel compact folded Yagi antenna, for its high gain and superiority in small size, is being interested in various electromagnetic application areas. But for its complex structure, as well as the strong coupling among the elements, making the optimization and design rather difficult. In this paper, the impact of multi-scale semi-solutions to the final performance of antenna is taken into account, a genetic algorithm applied to the multi-objective optimization of folded Yagi antenna is realized. To illustrate the feasibility and effectiveness of the algorithm, a three-element double-folded Yagi antenna is designed and fabricated, the measurements show good results in gain (7.40dBi) and perfect match in impedance (49.87-j0.49Ω), which fully verify the reliability of the algorithm.

关键词： optimization Antenna measurements Antenna arrays Impedance Algorithm design and analysis

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optimization of fuzzy controller based on multi-parent crossover dynamic evolutionary algorithm

Optimization of fuzzy controller based on multi-parent cross...

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International conference on Mechanic Automation and Control Engineering (MACE)

作者： Xuewen Xia Zengang Xiong Zhiming Li Yuanxiang Li Dept of Computer and Information Science Xiaogan College China 432000 State Key Laboratory of Software Engineering Wuhan University China 430072

Fuzzy control is a nonlinear control method; its performance depends on the fuzzy control rules, quantification factor and the scale factor. Because these multiple objectives are influence with each others, therefore it is very difficult to ensure the control effect only rely on expertise's experience. In this paper, dynamic evolutionary algorithm is adopted to optimize the multi-objectives in fuzzy controller. In dynamic evolutionary algorithm, based on the competitive relationship between particles' free energy and entropy in the phase space system, a new option strategy is put forward which is used to maintain species diversity; furthermore, multi-parent crossover operator is selected. This operator selects some individuals to form a space and then do searching in this space. This algorithm has strong ability to find the solutions of the problem, and it also run quickly compared with other traditional algorithms. Simulation results show that fuzzy controller optimized by the algorithm has good steady-state response time, steady-state error and overshoot.

关键词： Fuzzy control Evolutionary computation Steady-state optimization methods Laboratories software engineering Information science Educational institutions nonlinear dynamical systems Entropy

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HMM training by a hybrid of Chaos optimization and Baum-Welch algorithms for discrete speech recognition

HMM training by a hybrid of Chaos Optimization and Baum-Welc...

引用

International conference on Digital Content, Multimedia Technology and its Applications (IDC)

作者： Somayeh Cheshomi Rahati-Q Saeed Mohammah-R Akbarzadeh-T Islamic Azad university Mashhad Iran Ferdowsi University Mashhad Iran

HMM has high power to describe complex phenomena. The Baum-Welch (BW) algorithm is very popular estimation method that use for estimating HMM model parameters but it start with an initial guess and finally converge to a local optimum in practice. Chaos often exists in nonlinear systems. It has many good properties such as ergodicity, stochastic properties, regularity and high sensitivity to initial states. In this paper by use of these properties of chaos, an effective hybrid CHAOS-BW optimization method is proposed that uses the Chaos optimization algorithm to optimize the initial values of Baum Welch algorithm. This algorithm not only overcomes the shortcoming of becoming trapped in local optimum of the BW algorithm, but is also fast and requires less storage than other hybrid optimization algorithms such as GABW, PSOBW and GAPSOBW. Experimental results on Persian digit dataset show that the propose method has both qualities of global search as well as rapid convergence. Comparison with several other more conventional approaches also reveals superior performance of the proposed model.

关键词： Hidden Markov models Chaos

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high Resolution Sonar Image Segmentation by PSO Based Fuzzy Cluster Method

High Resolution Sonar Image Segmentation by PSO Based Fuzzy ...

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International conference on Genetic and Evolutionary Computing (ICGEC)

作者： Liu Hongpo Sun Jun Wu Hai Teng Shuhua Tan Zhiguo School of Software and Technology Henan Polytechnic Institute Nanyang China General Armament Department Military Representative Office Chengdu Chengdu China School of Electronic Science and Engineering National University of Defense Technology Changsha China

In this paper, we manage to use the clustering method realize sonar image segmentation. A particle swarm optimization (PSO) based FCM algorithm (PSO-FCM) is proposed which PSO incorporate with Fuzzy Clustering Method (FCM). The algorithm takes the clustering result of PSO as the initialization of the FCM, and uses fuzzy measures and fuzzy integrals to express the adapt function. At last, the algorithm is applied to the high resolution sonar image segmentation. Segmentation results of FCM and PSO-FCM for several sonar images are compared, which show that the PSO-FCM algorithm has better performance and fit for the sonar image segmentation better than the FCM does.

关键词： Clustering algorithms Image segmentation Sonar Pixel Particle swarm optimization Classification algorithms Image resolution

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A Block Motion Compensation Fast Algorithm Designed for RV40 Codec

A Block Motion Compensation Fast Algorithm Designed for RV40...

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International conference on Computational Intelligence and software Engineering (CiSE)

作者： Han Yu Yue Yang Sujuan Liu Jianxin Chen College of Electronic Information and Control Engineering Beijing University of Technology Beijing China

This paper proposed a whole new design for RV40(realvideo40) video decoder from FFmpeg(fast forward Moving Picture Experts Group),which is in lack of the ability to decode smoothly when decoding a period of high bit rate frames within some video streams. The design specially optimized the sub pixel interpolation algorithm, which is the core of motion compensation. The design uses detailed, different algorithms based on different frame types. Meanwhile a temporal related, flexible algorithm usage method is introduced to deal with the jamming in decoding sequence. The time consumed by calculating is reduced with an acceptable reduced frame appearance. This design is used on a test platform for testify. The result returned shows that this design can greatly promote decoding performance up to 50% while reducing a little picture performance.

关键词： Decoding Motion compensation Pixel Streaming media optimization Codecs Interpolation

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Intelligent Task Mapping Using Machine Learning

Intelligent Task Mapping Using Machine Learning

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International conference on Computational Intelligence and software Engineering (CiSE)

作者： Dirk Tetzlaff Sabine Glesner Group Software Engineering of Embedded Systems Berlin Institute of Technology Germany

Task scheduling and task allocation, which are vital parts of mapping parallel programs to concurrent architectures, must take into account the interprocessor communication, whose overheads have emerged as the major performance limitation in parallel applications. Furthermore, its power consumption is an important research focus which must be addressed. Finding an optimal solution requires information about the runtime behavior, which is not known at compile time. Moreover, the computational complexity leads to heuristic approaches based on conservative assumptions that are unable to exploit all of the program's optimization potential. In this paper, we propose a novel approach to automatically generate architecture- and application-specific heuristics for power- and communication-aware task mapping using machine learning techniques to predict how programs behave at runtime. The key advantage of machine learning techniques is their ability to find relevant information in a high-dimensional space. This yields more precise heuristics than those based on pure static assumptions, as our experimental results show. Because learning is done in an off-line training phase once per architecture, the compile time itself is not extended as in other heuristic approaches like genetic or evolutionary algorithms.

关键词： Runtime Machine learning Heuristic algorithms Computer architecture Training Processor scheduling Resource management

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Application of Ant Colony optimization Algorithm to Ramp Metering

Application of Ant Colony Optimization Algorithm to Ramp Met...

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International conference on Information Engineering and Computer Science (ICIECS)

作者： Jian Xiong Xinrong Liang Licheng Liang College of Information Wuyi University Jiangmen Guangdong China

On-ramp control is the most effective and extensive way to improve freeway capacity. A proportional-integral (PI) control method based on ant colony optimization (ACO) is proposed to regulate the number of vehicles entering a freeway entrance point. First, a macroscopic traffic flow model is established. Then the basic principles of ant colony algorithm are formulated and the steps of ACO algorithm in optimizing the PI parameters are given. In conjunction with nonlinear feedback theory, on-ramp PI controller optimized by ant colony algorithm is designed. Finally, the controller is simulated in MATLAB software. The results show that the ACO-based ramp metering controller has fast response speed, high computation efficiency, and good dynamic and steady-state performance.

关键词： Traffic control Vehicles Mathematical model Algorithm design and analysis Ant colony optimization Adaptation model

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Plowing PSO: A novel approach to effectively initializing particle swarm optimization

Plowing PSO: A novel approach to effectively initializing pa...

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International conference on Computer Science and Information Technology (CSIT)

作者： Mohammad Sadegh Norouzzadeh Mohammad Reza Ahmadzadeh Maziar Palhang Electrical & Computer Engineering Department Isfahan University of Technology Isfahan Iran

Particle swarm optimization (PSO) is an optimization algorith that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multimdoal functions. To improve PSO performance on global optimization problems, this paper proposes a novel approach, called Plowing PSO algorithm, through introductng a new operator to PSO. The proposed approach combines the exploration ability of random search with the features of PSO. Our approach is validated using ten common complex unimodal/multimodal bencmark functions. The simulation results demostrate that the proposed approach is superior in avoiding premature convergence to standard PSO, and five variation of it. Therefore, the Plowing PSO algorithm is successful in improving standard PSO to solve complex numerical function optimization problems.

关键词： optimization Numerical models Accuracy software algorithms Noise measurement

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Novel PSO based FPGA placement techniques

Novel PSO based FPGA placement techniques

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International conference on Computer and Communication Technology (ICCCT)

作者： Prakash Kumar Rout D. P. Acharya G. Panda Department of Electronics and Communications Engineering National Institute of Technology Rourkela India Department of Electrical Sciences Indian Institute of Technology Bhubaneswar India

Digital ICs for electronic systems are fast realized on Field programmable gate array (FPGA). The reconfigurability of FPGA has made this mode of digital circuit synthesis more popular among the system designers. But unlike other ICs it provides a restricted hardware structure for circuit implementation and hence the computer aided design (CAD) software is also constrained. The placement being a very vital step in the design process needs to be performed optimally for high performance circuits. In this work novel techniques for placement based on simple particle swarm optimization (PSO), constricted PSO and time varying inertia weight (TVIW) PSO are proposed taking bounding box cost into consideration. The results of simulation reveal a competitive performance of the circuits implemented. The technique proposed here also offer faster convergence to a placement solution. The performance of a single BCD counter circuit is studied in details by using the different PSO algorithms. The netlist generated from the Xilinx design tool is used for placement and optimization results are reported here.

关键词： Field programmable gate arrays Cost function Algorithm design and analysis Particle swarm optimization Wire Convergence

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Digital circuit placement in FPGA based on efficient particle swarm optimization techniques

Digital circuit placement in FPGA based on efficient particl...

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International conference on Industrial and Information Systems, ICIIS

作者： P.K. Rout D.P. Acharya G. Panda Department of Electronics and Communications Engineering National Institute of Technology Rourkela India Department of Electrical Sciences Indian Institute of Technology Bhubaneswar India

Field programmable gate array (FPGA) is a widely used programmable integrated circuit (IC) for fast realization of digital circuits in all electronic systems. Its reconfigurability has made this mode of digital circuit synthesis more popular among the system designers. But unlike other ICs it provides a restricted hardware structure for circuit implementation and hence the computer aided design (CAD) software is also constrained. The placement being a very vital step in the design process needs to be performed optimally for high performance circuits. In these work novel techniques for placement based on constricted particle swarm optimization (PSO), adaptive PSO and time varying inertia weight (TVIW) PSO are proposed. The results of simulation reveal a competitive performance of the circuits implemented. The technique proposed here also offer faster convergence to a placement solution. The performance of a single BCD counter circuit is studied in details by using the different PSO algorithms. The netlist generated from the Xilinx design tool is used for placement and optimization results are reported here.

关键词： Field programmable gate arrays Particle swarm optimization Integrated circuit modeling Cost function Algorithm design and analysis Convergence

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