检索结果-内蒙古大学图书馆

24th International Conference Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Talaska, Tomasz Dlugosz, Rafal Pedrycz, Witold UTP Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn Ul Kaliskiego 7 PL-85796 Bydgoszcz Poland Delphi Poland SA Ul Podgorki Tynieckie 2 PL-30399 Krakow Poland Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

ISBN: (纸本)9788363578121

In this paper, we present a concept of a transistor level implementation of the particle swarm optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.

关键词： particle swarm optimization algorithm CMOS Artificial Neural Networks Analog signal processing

来源：评论

学校读者我要写书评

暂无评论

Optimizing Effective Fusion Representation by particle swarm optimization algorithm for Protein Sub-nuclear Location 6

Optimizing Effective Fusion Representation by Particle Swarm...

引用

6th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Yue, Yaoting Wang, Shunfang Yunnan Univ Sch Informat Sci & Engn Kunming 650504 Yunnan Peoples R China

ISBN: (纸本)9781538604939

Feature representation contains the more plentiful information of original protein sequence, the more beneficial for protein sub-nuclear localization. Inspired by this idea, this paper proposed a novel two-feature integration method, whose fusion parameter was optimized via the particle swarm optimization algorithm (PSO), for obtaining a more effective representation. Therefore, a new fusion representation, called AACPSSM, would be formed by integrating two kinds of single feature expression, amino acid composition (AAC) and position specific scoring matrix (PSSM). Due to the high dimensional characteristics of protein data, kernel linear discriminant analysis (KLDA) was used to conduct the data dimension reduction. Last, to evaluate validity of our proposed approach, a benchmark dataset and KNN classifier were used to carry out the numerical experiments. And the final Jackknife test experimental results prove that our proposed fusion representation AACPSSM largely outperforms the single one, AAC and PSSM.

关键词： Sub-nuclear location particle swarm optimization algorithm fusion parameter AACPSSM KLDA

来源：评论

学校读者我要写书评

暂无评论

Research on location assignment model of intelligent warehouse with RFID and improved particle swarm optimization algorithm

Research on location assignment model of intelligent warehou...

引用

International Conference on Computer Systems, Electronics and Control (ICCSEC)

作者： Yang, Lvqing Zheng, Yan Xu, Yezi Bai, Youjing Xiamen Univ Dept Software Engn Xiamen 361005 Peoples R China

ISBN: (纸本)9781538635735

Reasonable warehouse storage planning and assignment is the key to reduce the product storage and retrieve time and improve warehouse operation efficiency. Moreover, capturing and sharing the information of warehouse in real time is the premise of warehouse location assignment, and internet of tings has provided this information required by leveraging the growing ubiquity of radio-frequency identification (RFID). Firstly this paper describes the layout of intelligent warehouse, then a multi-objective intelligent warehouse location assignment model is proposed with many constrain rules. Finally we develop an improved particle swarm optimization algorithm to solve the model, and verify the effectiveness of the model.

关键词： RFID intelligent warehouse location assignment particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive particle swarm optimization algorithm Based on Levy Flights Mechanism

Adaptive Particle Swarm Optimization Algorithm Based on Levy...

引用

Chinese Automation Congress (CAC)

作者： Du, Zhongzhou Li, Si Sun, Yi Li, Nana Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538635247

This article aims to the problems that the particle swarm optimization (PSO) algorithm has slow convergence and easy to fall into local optimum, provides an improved adaptive particle swarm optimization algorithm based on Levy flight mechanism (LFAPSO). The long jumps of Levy flight will step out of the local optimum in the local search. The convergence speed and accuracy of the LFAPSO algorithm are certified on 6 typical test functions. The simulation results show that the LFAPSO algorithm is obviously more successful than chaotic particle swarm optimization algorithm with adaptive mutation (ACPSO) and adaptive particle swarm optimization (APSO) algorithm in convergence performance and robustness. Furthermore, the results demonstrate the LFAPSO algorithm works better to solve the multidimensional function. The method will be used to different optimization problems such as scheduling problems, training neural networks, image segmentation, etc.

关键词： adaptive particle swarm optimization algorithm levy flight

来源：评论

学校读者我要写书评

暂无评论

Cloud Task Scheduling Based on Chaotic particle swarm optimization algorithm

Cloud Task Scheduling Based on Chaotic Particle Swarm Optimi...

引用

International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Li Yingqiu Li Shuhua Gao Shoubo Dalian Neusoft Inst Informat Dept Software Engn Dalian 116023 Liaoning Peoples R China

ISBN: (纸本)9781509060610

In cloud computing environment, there is a large quantity of submitted tasks by users. How to schedule these massive tasks efficiently and reasonably becomes a serious challenge. This paper proposes a Chaotic particle swarm optimization algorithm (CPSO) to overcome the problems of Standard particle swarm algorithm such as premature convergence and low accuracy. Firstly, in initial process, chaotic sequence is introduced to enhance the diversity of particles. Then, an effective diagnosis mechanism of premature is adopted to determine local convergence and algorithm correction is performed by chaotic mutation, which could activate the particles in stagnation and make them escape from local optimum. Simulation experiments show that the proposed approach is feasible and effective.

关键词： Cloud Computing Task Scheduling particle swarm optimization algorithm Chaotic Sequence Chaotic Mutation

来源：评论

学校读者我要写书评

暂无评论

Short-term Load Forecasting Model Based on Ridgelet Neural Network Optimized by particle swarm optimization algorithm 8

Short-term Load Forecasting Model Based on Ridgelet Neural N...

引用

2017 IEEE 8th International Conference on Software Engineering and Service Science

作者： Wang Qun Zhang Yingbin Zhu Xinying Qiu Youming Wang Yize Zhang Zhisheng Zaozhuang Electric Power Company School of Automation Engineering Qingdao University

ISBN: (纸本)9781538604984;9781538604977

In this paper, the short-term load forecasting model based on ridgelet neural network optimized by the particle swarm optimization algorithm is proposed. The ridgelet neural network is simulated based on the visual cortex of the human brain. Compared with the traditional neural network, the neurons of the ridgelet neural network have directional characteristics, which can receive more dimensional information and have the ability to process higher dimensional data, and can better approximate nonlinear high dimensional functions. The particle swarm optimization algorithm is used to train the ridgelet neural network in this paper. The learning algorithm can not only speed up the convergence of the network, but also greatly reduce the probability of getting into the local minimum in the learning process. Through the simulation using the actual load data of power grid, simulation results show that the proposed model can effectively realize load forecasting and achieve the engineering accuracy requirements.

关键词： short-term load forecasting ridgelet neural network particle swarm optimization algorithm electric power system

来源：评论

学校读者我要写书评

暂无评论

Multi-Constrained Routing Based on particle swarm optimization and Fireworks algorithm 44

Multi-Constrained Routing Based on Particle Swarm Optimizati...

引用

44th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Hu, Youbing Wang, Kun Wan, Jinjiang Wang, Kaidong Hu, Xia Xidian Univ Sci & Technol Commun Networks Lab Shijiazhuang Sch Comp Sci & Technol Xian Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China Elect Technol Grp Corp Res Inst China 54 Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781509066841

This paper sets up a mathematical model that satisfies the multiconstrained routing optimization problem. By adding a penalty, multiple constraints are mapped to a fitness that satisfies multiple constraints. Then, it uses a heuristic routing algorithm based on particle swarm optimization (PSO) to perform heuristic routing search. Introducing the fireworks algorithm (FWA) based on the PSO search algorithm, our algorithm searches the optimal solution more quickly. Besides, it reduces the defect of PSO falling into the local optimum. Simulation shows the algorithm can effectively solve the multiconstrained routing problem in large-scale networks. While searching for optimal solutions, the success rate of the algorithm is about 5.21% higher than that of the standard PSO algorithm. That is improved by using the ant colony algorithm. The PSO-ACO algorithm is about 2.57% higher than the problem. The average cost of the final search is about 4.36% higher than that of the standard PSO algorithm. It is about 1.34% higher than the PSO-ACO algorithm improved by the ant colony algorithm.

关键词： multiple constraints routing Quality of Service penalty function particle swarm optimization algorithm fireworks algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network

引用

CONSTRUCTION AND BUILDING MATERIALS 2016年 119卷 277-287页

作者： Mashhadban, Hadi Kutanaei, Saman Soleimani Sayarinejad, Mohammad Ali Saroyeh Univ Dept Civil Engn POB 404 Sari Iran Islamic Azad Univ Qaemshahr Branch Young Researchers & Elite Club Qaemshahr Iran Islamic Azad Univ Dept Civil Engn Qaemshahr Iran

Intelligence system is a field of computer science that designs and studies efficient computational methods for solving problem. The purpose of present study is to investigate the effects of fibers on the performance of self compacting concrete (SCC). In this experiment study, 9 concrete mixtures containing two types of fibers (polyphenylene sulfide: 0.1, 0.2, 0.3 and 0.4% by volume and steel: 0.1, 0.2, 0.3 and 0.4% by volume) and unreinforced samples have been tested and compared. Fresh, mechanical and durability properties and ultrasonic pulse velocity of all SCC mixtures were evaluated. Then this experimental data was used to train the feed forward artificial neural network type. Finally the trained ANN (artificial neural network) and PSOA (particle swarm optimization algorithm) are used to generate a polynomial model for predicting SCC properties. The obtained results showed that the mechanical properties can be significantly improved by fiber reinforcement and workability of the SCC decreases with increasing fiber content. Moreover, steel fibers have better performance with relation to mechanical properties than polyphenylene sulfide fibers. In addition, PSOA integrated with the ANN is a flexible and accurate method in prediction of mechanical properties of fiber reinforced SCC properties. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Fiber Self compacting concrete Artificial neural network particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Based on particle swarm optimization algorithm of Cloud Computing Resource Scheduling in Mobile Internet

引用

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第6期9卷 25-34页

作者： Lin, Yong Qingdao Vocat & Tech Coll Hotel Management Qingdao Shandong Peoples R China

Mobile Internet due to the limitation of the mobile terminal power supply, transmission and calculation of the need to adopt energy saving strategy, also due to the terminal mobility, mobile Internet topology change. Therefore, mobile Internet cloud computing resources allocation need both energy efficiency and assure the time characteristics of mobile business;Aiming at this problem, this paper presents a maximum energy efficiency optimization, in the restrictive conditions at the same time, guarantee the minimum time delay the business. According to the characteristics of the optimization problems, both the distribution of the improved algorithm is proposed, the algorithm based on particle swarm optimization (pso) algorithm, build the search direction matrix of orientation, the simulation results show that the proposed allocation algorithm can effectively improve the efficiency of energy utilization, and ensure that the time delay of the business requirements.

关键词： The Mobile Internet Cloud Computing particle swarm optimization algorithm Resource Scheduling

来源：评论

学校读者我要写书评

暂无评论

Vehicle Fuel Consumption Prediction Based on Least Squares Support Vector Machine Optimized by Improved particle swarm optimization algorithm

Vehicle Fuel Consumption Prediction Based on Least Squares S...

引用

第19届亚太汽车工程年会暨2017中国汽车工程学会年会

作者： Liu Yu Li Mengliang Xu Yueyun China Automotive Technology&Research Center

Firstly,this paper summarizes the characteristics of vehicle running characteristics and design parameters,which have influence on vehicle fuel ***,200 vehicles test results are used as training samples,with sensitive features and fuel consumption of type approve test as the input parameters,and the actual vehicle fuel consumption as output *** vehicle fuel consumption prediction model based on Least squares support vector machine(LSSVM) optimized by the improved particle swarm optimizationalgorithm(IPSO) is ***,the vehicle fuel consumption prediction model is used to predict the fuel consumption of another 100 *** results show that the prediction error of test samples are less than 5%,and the fuel consumption prediction model proposed in this paper has fully considered the impact of vehicle operating characteristics and design parameters on fuel *** addition,the fuel consumption predictionmodel has high prediction accuracy and reliability than some traditional methods such as back propagation neural network(BPNN).

关键词： least squares support vector machine particle swarm optimization algorithm vehicle fuel consumption prediction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：