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6th International Conference on Machine Learning and Cybernetics

作者： Ma, Ming Zhang, Li-Biao Beihua Univ Informat Manage Ctr Jilin 132013 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130122 Peoples R China

ISBN: (纸本)9781424409723

We have proposed an algorithm to optimize fuzzy neural network based on hierarchical genetic algorithm. It can evolve both the fuzzy neural network's topology and weighting parameters. In a real problem, it can automatically obtain the near-optimal structure of fuzzy neural network according to the requirements. Numerical simulations show the effectiveness of the proposed algorithm.

关键词： hierarchical genetic algorithm fuzzy neural network optimize

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OPTIMIZING A FUZZY NEURAL NETWORK WITH A hierarchical genetic algorithm

OPTIMIZING A FUZZY NEURAL NETWORK WITH A HIERARCHICAL GENETI...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： MING MA LI-BIAO ZHANG Information Manage Center Beihua University Jilin 132013 China College of Computer Science and Technology Jilin University Changchun 130012 China

We have proposed an algorithm to optimize fuzzy neural network based on hierarchical genetic *** can evolve both the fuzzy neural network's topology and weighting *** a real problem, it can automatically obtain the near-optimal structure of fuzzy neural network according to the *** simulations show the effectiveness of the proposed algorithm.

关键词： hierarchical genetic algorithm Fuzzy neural network Optimize

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Three-phase inverter fault diagnosis based on optimized neural networks

Three-phase inverter fault diagnosis based on optimized neur...

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The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Fan Bo Dong Ming Zhao Jie Zhang Qiang The Missile College Air Force Engineering University Shan xi San yuan China

The problem that how to use hierarchical genetic algorithm to determine the structure and parameters of neural networks was studied. Utilized the two grade coding structure of the hierarchical genetic algorithm to solve the ancient problem that when optimize the neural networks' structure, connection weights, threshold at the same time, the efficiency was low. Furthermore, an improved adaptive hierarchical genetic algorithm was educed, and it improved the shortage of the normal adaptive hierarchical genetic algorithm. At last, the improved adaptive genetic algorithm is used to the fault diagnosis of three-phase inverter, the simulation result shown the method was correct and applied.

关键词： hierarchical genetic algorithm neural networks three-phase SPWM inverter fault diagnosis

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USE hierarchical genetic PARTICLE FILTER TO FIGURE ARTICULATED HUMAN TRACKING

USE HIERARCHICAL GENETIC PARTICLE FILTER TO FIGURE ARTICULAT...

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IEEE International Conference on Multimedia and Expo (ICME 2008)

作者： Ye, Long Zhang, Qin Guan, Ling Commun Univ China Informat Engn Sch Beijing Peoples R China Ryerson Univ Ryerson Multimedia Res Lab Toronto ON Canada

ISBN: (纸本)9781424425709

Using particle filter to track human movement, a key problem is how to draw samples in high-dimensional state space. In this paper, we present a novel framework of particle filtering, namely hierarchical genetic Particle Filter (HGPF), to improve the efficiency of samples by a hierarchical evolutionary detection. As a result, we can obtain reasonably distributed samples thus translating into reliable tracking performance. Finally, we apply the technique to 2D articulated human movement tracking. Result demonstrates the effectiveness of HGPF in solving the tracking problem like self-occlusion and cluttered background.

关键词： Particle Filtering hierarchical genetic algorithm Articulated Human Movement Tracking Self-Occlusion High-dimensional Optimization

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A rule-based availability-driven cosynthesis scheme

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DESIGN AUTOMATION FOR EMBEDDED SYSTEMS 2007年第2-3期11卷 193-222页

作者： Chakraverty, Shampa Kumar, Anil Netaji Subhas Inst Technol Fac Div Comp Engn New Delhi 110058 India Univ Delhi Fac Technol Delhi 110007 India

The co-synthesis of hardware-software systems for complex embedded applications has been studied extensively with focus on various qualitative system objectives such as high speed performance and low power dissipation. One of the main challenges in the construction of multiprocessor systems for complex real time applications is provide high levels of system availability that satisfies the users' expectations. Even though the area of hardware software cosynthesis has been studied extensively in the recent past, the issues that specifically relate to design exploration for highly available architectures need to be addressed more systematically and in a manner that supports active user participation. In this paper, we propose a user-centric co-synthesis mechanism for generating gracefully degrading, heterogeneous multiprocessor architectures that fulfills the dual objectives of achieving real-time performance as well as ensuring high levels of system availability at acceptable cost. A flexible interface allows the user to specify rules that effectively capture the users' perceived availability expectations under different working conditions. We propose an algorithm to map these user requirements to the importance attached to the subset of services provided during any functional state. The system availability is evaluated on the basis of these user-driven importance values and a CTMC model of the underlying fail-repair process. We employ a stochastic timing model in which all the relevant performance parameters such as task execution times, data arrival times and data communication times are taken to be random variables. A stochastic scheduling algorithm assigns start and completion time distributions to tasks. A hierarchical genetic algorithm optimizes the selections of resources, i.e. processors and busses, and the task allocations. We report the results of a number of experiments performed with representative task graphs. Analysis shows that the co-synthesis tool we

关键词： reliability and availability graceful degradation hardware-software co-synthesis task allocation and scheduling stochastic task graphs hierarchical genetic algorithm distributed embedded real-time systems continuous time Markov chains

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A novel clustering approach using hierarchical genetic algorithms

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2005年第3期11卷 143-153页

作者： Lai, CC Natl Univ Tainan Dept Comp Sci & Informat Engn Tainan 700 Taiwan

Clustering is a central task for data analysis that partitions heterogeneous data sets into groups of more homogeneous characteristics. However, most of clustering algorithms require the user to provide the number of clusters as input. In this paper, we consider the automatic clustering problem that one has to partition data points without any a priori knowledge about the correct number of clusters. The hierarchical genetic algorithm (HGA) is employed for automatically searching the number of clusters as well as properly locating the centers for clusters. The well-known Davies-Bouldin index is adopted as a measure of the validity of the clusters. Experimental results on artificial and real-life data sets are given to illustrate the effectiveness of the proposed approach.

关键词： clustering hierarchical genetic algorithm Davies-Bouldin index

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Control strategy for multi-objective coordinate voltage control using hierarchical genetic algorithms

Control strategy for multi-objective coordinate voltage cont...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Ma, H. M. Man, K. F. Hill, D. J. City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)0780394836

hierarchical genetic algorithm (HGA) is proposed for optimizing the power voltage control systems according to number of control actions. The advantage of HGA is its capability in control the parametric genes of chromosome. In this paper, we apply HGA to find out the optimal solution for coordinate voltage control in a simple six buses power system. The number of control actions is fixed from one to six by HGA. Because of the multi-objective classification of the obtained solutions, all these solutions could therefore form a landscape of control pattern which is aptly applicable to the control purpose of coordinate power control system. The application of the proposed paradigm is demonstrated through simulation and the results obtained suggested that the speed of voltage recovery in some degree related with the number of actions of control when emergency happened. The effective of control is influenced by the location of control devices and the system structure.

关键词： hierarchical genetic algorithm coordinate voltage control multi-objective optimization Pareto front

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Power system stabilizer tuning based on multiobjective design using hierarchical and parallel micro genetic algorithm

Power system stabilizer tuning based on multiobjective desig...

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International Conference on Power System Technology (POWERCON 2004)

作者： Hongesombut, K Mitani, Y Dechanupaprittha, S Ngamroo, I Pasupa, K Tippayachai, J Kyushu Inst Technol Dept Elect Engn Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)0780386108

In order to achieve the optimal design based on some specific criteria by applying conventional techniques, sequence of design, selected location of PSSs are critical involved factors. This paper presents a method to simultaneously tune PSSs in multimachine power system using hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel micro-GA) based on multiobjective function comprising the damping ratio, damping factor and number of PSSs. First, the problem of selecting proper PSS parameters is converted to a simple multiobjective optimization problem. Then, the problem will be solved by a parallel micro GA based on HGA. The stabilizers are tuned to simultaneously shift the lightly damped and undamped oscillation modes to a specific stable zone in the s-plane and to self identify the appropriate choice of PSS locations by using eigenvalue-based multiobjective function. Many scenarios with different operating conditions have been included in the process of simultaneous tuning so as to guarantee the robustness and their performance. A 68-bus and 16-generator power system has been employed to validate the effectiveness of the proposed tuning method.

关键词： hierarchical genetic algorithm multiobjective design parallel micro genetic algorithm power system stabilizer tuning

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A hierarchical genetic algorithm for reliability based design of geometrically non-linear composite structures

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COMPOSITE STRUCTURES 2001年第1期54卷 37-47页

作者： António, CAC Univ Porto Fac Engn P-4200465 Oporto Portugal

A design optimisation methodology based on structural reliability of beam reinforced composite shell structures with non-linear geometric behaviour is proposed. The formulation involves probabilistic stress, displacement and buckling constraints. The structural integrity is evaluated through the reliability index using a Lind-Hasofer second-order-second-moment approximation method together with the Newton-Raphson iterative procedure and the arc-length method. The random variables are the mechanical properties of the laminates treated as homogeneous orthotropic materials. A new methodology based on an evolutionary strategy searching the global most probable failure point (MPP) for composite structures under non-linear geometric behaviour is proposed. The optimal design performs on a hierarchical genetic algorithm (HGA) based on weight minimisation under prescribed reliability constraints. The design variables are the ply angle, the ply thickness and the geometric variables associated with the cross sections of the stiffeners. To demonstrate the applicability of the proposed developments, optimisation problems are presented. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： reliability geometrically non-linear composite structures hierarchical genetic algorithm

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Implementation of advanced genetic algorithm to modern power system stabilization control

Implementation of advanced genetic algorithm to modern power...

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IEEE PES Power Systems Conference and Exposition

作者： Hongesombut, K Mitani, Y Kyushu Inst Technol Dept Elect Engn Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)078038718X

This paper focuses on the use of advanced techniques in genetic algorithm for solving power system stabilization control problems. At the outset, the proposed hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel micro-GA) are proposed and then they win be extended to solve two example problems. In the first example, these techniques are applied to simultaneously tune power system stabilizers (PSSs). The PSSs are optimally tuned to simultaneously shift the lightly damped and undamped oscillation modes to a stable zone in the s-plane and to self identify the appropriate PSS locations. In second example, parallel micro-GA is used to design the fuzzy logic controller (FLC) of STATCOM. The applied technique for designing a FLC helps us save time and does not require experts for designing. From these results, we can see that these advanced techniques provide enhanced versatility for solving problems in power system stabilization control.

关键词： power system stabilization advanced genetic algorithm hierarchical genetic algorithm parallel micro genetic algorithm

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