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检索条件"主题词=Particle swarm optimization algorithm"

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Research on Prediction of Egg Freshness based on Improved GRNN

Research on Prediction of Egg Freshness based on Improved GR...

引用

International Conference on Mechatronics and Semiconductor Materials (ICMSCM 2013)

作者： Yang, Jian Shi, Ying Li, Xiuying Wang, Xin Yin, Yingying Jilin Agr Univ Informat Technol Teaching & Management Ctr Changchun Peoples R China

ISBN: (纸本)9783037859391

The purpose of this paper is to add particle swarm optimization algorithm to Generalized Regression Neural Network for predicting egg Haugh value and evaluating freshness degree of eggs. Firstly process the egg images with light-transmitting were obtained by the computer vision device including denoising, threshold segmentation, conversing HSI Color model and calculating the averages of hue, saturation, and intensity in the center of the image. Secondly analyze GRNN, and then particle swarm algorithm to optimize according to the predicted formula being derived. Thirdly train Improved GRNN and predicate Haugh value by HSI parameter data as the sample. The value of residual errors of Improved GRNN model are 6.38, the correct discerning rate of grading table eggs is 91.2%. It proves better than traditional BP neural network in terms of predicted accuracy and robustness.

关键词： Freshness degree of eggs Nondestructive detection Generalized Regression Neural Network Machine Vision particle swarm optimization algorithm

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Bandwidth and Power Allocation for Two-Way Relaying in Overlay Cognitive Radio Systems

Bandwidth and Power Allocation for Two-Way Relaying in Overl...

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IEEE Global Communications Conference (GLOBECOM)

作者： Alsharoa, Ahmad Ghazzai, Hakim Yaacoub, Elias Alouini, Mohamed-Slim King Abdullah Univ Sci & Technol KAUST Thuwal Makkah Province Saudi Arabia QMIC Doha Qatar

ISBN: (纸本)9781479935123

In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) set-up is investigated. In the CR overlay mode, primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. In our framework, we propose that the CUs are allowed to allocate a part of the PUs spectrum to perform their cognitive transmission. In return, acting as an amplify-and-forward two-way relays, they are used to support PUs to achieve their target data rates over the remaining bandwidth. More specifically, CUs acts as relays for the PUs and gain some spectrum as long as they respect a specific power budget and primary quality-of-service constraints. In this context, we first derive closed-form expressions for optimal transmit power allocated to PUs and CUs in order to maximize the cognitive objective. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the optimal relay amplification gains and optimal cognitive released bandwidths as well. Our numerical results illustrate the performance of our proposed algorithm for different utility metrics and analyze the impact of some system parameters on the achieved performance.

关键词： Overlay cognitive radio systems two-way relaying technique particle swarm optimization algorithm

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Resource Allocation Problem in Port Project Portfolio Management 7

Resource Allocation Problem in Port Project Portfolio Manage...

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7th International Joint Conference on Computational Sciences and optimization (CSO)

作者： Ye, Kaibin Shi, Xiaolun Li, Huijian Shi, Ning South China Agr Univ Coll Sci Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Business Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479953721

Due to the increased complexity of port operations, port project portfolio management has become more and more essential. However, in port project portfolio management, the demand for resources is usually uncertain. In order to reduce the cost and the difficulty of resource allocation, we pay more attention to balancing resource allocation than minimizing the maximum value of the consumption of resources. Thus we consider a resource leveling problem in port project portfolio management. In this paper, we introduce a combination of entropy weight and particle swarm optimization algorithm. This proposed method is applied to a case study. And the results show that this method can achieve satisfactory performance in practice.

关键词： port project portfolio management resource leveling particle swarm optimization algorithm entropy weight

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Computing Single Step Operators of Logic Programming in Radial Basis Function Neural Networks

Computing Single Step Operators of Logic Programming in Radi...

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21st National Symposium on Mathematical Sciences (SKSM)

作者： Hamadneh, Nawaf Sathasivam, Saratha Choon, Ong Hong Univ Sains Malaysia Sch Math Sci Usm 11800 Penang Malaysia

ISBN: (纸本)9780735412415

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T-p : I -> I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

关键词： Single step operator particle swarm optimization algorithm recurrent radial basis function neural networks

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Performance optimization of DOA Estimation in Non-Uniform Linear Antenna Array by PSO algorithm

Performance Optimization of DOA Estimation in Non-Uniform Li...

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2nd International Conference on Mechanical Design and Power Engineering (ICMDPE 2013)

作者： Han, Huilian Wan, Liangtian Si, Weijian Beijing Aerosp Automat Control Inst Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing Peoples R China Harbin Engn Univ Informat & Commun Engn Coll Harbin Peoples R China

ISBN: (纸本)9783038350019

In order to estimate the coherent source, a modified multiple signal classification (MUSIC) algorithm is introduced. And a novel arrangement method for the non-uniform linear array by particle swarm optimization (PSO) algorithm is proposed. This method needs merely a signal source whose direction-of-arrival (DOA) has been exactly known. The proposed method has a simple processing and a strong stabilization. It could be applied to optimized arbitrary array configuration. The simulation verifies that the performance of DOA estimation is improved effectively, which has proved the validity of the proposed method.

关键词： DOA estimation non-uniform array particle swarm optimization algorithm

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A Fuzzy Multi-Objective optimization Method Solving the Output of Energy Storage System 6

A Fuzzy Multi-Objective Optimization Method Solving the Outp...

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6th International Symposium on Parallel Architectures, algorithms, and Programming (PAAP)

作者： Wang, Yiyi Huang, Mei Zhang, Caiping Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr NANTEC Beijing Peoples R China

ISBN: (纸本)9781479938445

The electricity consumption of a charging station is obviously fluctuant. An energy storage system is an effective device to improve the load variation of the charging station. In order to give full play to energy storage system, this paper proposes a charging and discharging control strategy of the storage system considering multi-objective including stabilizing load and economizing electric consumption, changing a bi-objective problem into a single objective problem with weights after fuzzy processing. This paper solves the problem based on particle swarm optimization algorithm, and considers constraints including the demand of uninterruptible power supply and SOC at given time, which has greater practical significance. In this paper, the data of a practical integrated smart grid construction project is taken into considered. Finally, we verify the effectiveness and feasibility of the optimization strategy after simulation analysis.

关键词： energy storage system charging scheduling electric vehicles particle swarm optimization algorithm multi-objective optimization

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Identifying New Prognostic Features for Remaining Useful Life Prediction 16

Identifying New Prognostic Features for Remaining Useful Lif...

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16th International Power Electronics and Motion Control Conference and Exposition (PEMC)

作者： Boukra, Tahar Lebaroud, Abdesselam Univ 20 Aout 1955 Skikda Dept Genie Elect Skikda Algeria

ISBN: (纸本)9781479920600

An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the existence of the fault to failure data is rare and the development of degradation based model is difficult, we must be addressed to the identification of new features having the qualities mentioned above. This paper present an new selection method based upon the particle swarm optimization (PSO) algorithm to identify the advanced prognosis feature and the Hidden Semi Markov Model (HSMM) for the prediction of the remaining useful life. This method was validated on a set of experimental data collected from bearing failures.

关键词： Prognostics Remaining Useful Life particle swarm optimization algorithm Feature selection Hidden Semi Markov Model

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Bio Inspired Approach for Load Balancing to Reduce Energy Consumption in Cloud Data Center

Bio Inspired Approach for Load Balancing to Reduce Energy Co...

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International Conference on Communication, Control and Intelligent Systems

作者： Akhil Goyal Navdeep S. Chahal Software Technology Division C-DAC

ISBN: (纸本)9781467375429

Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as providers. But along with advancement every technology is also associated with some ill effects. On same path, cloud computing also accompanies a serious issue with it and that issue is energy consumption. In this paper Firefly algorithm has been selected as a proposed bio-inspired approach to perform load balancing to reduce energy consumption in cloud data center. Further, the results are compared with particle swarm optimization algorithm (PSO). The energy consumed in case of Firefly algorithm is less than energy consumed in PSO algorithm.

关键词： Cloud Computing Firefly algorithm particle swarm optimization algorithm Energy Consumption Load Balancing

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STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON particle swarm ANT COLONY optimization algorithm

STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICL...

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2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)

作者： Yang, Zhengqiu Liu, Meiling Xiu, Jiapeng Liu, Chen Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

ISBN: (纸本)9781467318556;9781467318570

Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particle swarm to solve cloud computing environment resources allocation problem.

关键词： particle swarm optimization algorithm Ant colony optimization algorithm Cloud resource allocation

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Nonlinear Inertia Weigh particle swarm optimization combines Simulated Annealing algorithm and Application in Function and SVM optimization

Nonlinear Inertia Weigh Particle Swarm Optimization combines...

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3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Jiao Bin Xu Zhixiang Shanghai DianJi Univ Sch Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783037852866

This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing(SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM.

关键词： particle swarm optimization algorithm inertia weight simulated annealing algorithm function optimization parameter optimization

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