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检索条件"主题词=Particle swarm Optimization Algorithm"

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Application of Improved Wavelet Neural Network in MBR Flux Prediction 16

Application of Improved Wavelet Neural Network in MBR Flux P...

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16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Cai, Guoshuai Li, Chunqing Tianjin Polytech Univ Coll Comp Sci & Software 399 Bin Shui Xi Rd Tianjin 300387 Peoples R China

ISBN: (纸本)9781509055074

Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment, but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane fouling is decline of MBR membrane flux, and the effect of MBR sewage treatment is directly affected by the decrease of membrane flux. In order to predict MBR membrane flux accurately and rapidly, the forecasting model of MBR membrane flux based on particle swarm improving wavelet neural network algorithm (PSO_WNN) was established. In view of the complexity of the MBR membrane fouling factor, in the beginning, the main components of the factors affecting the flux of MBR membrane were analyzed. The important factor is extracted as the input of the PSO_WNN prediction model, and the membrane flux is used as the output. Then, the PSO_WNN simulation model is established, and the prediction results are obtained by using the model. By comparing the predicted data and experimental data, the predictive accuracy of this algorithm is high on the membrane flux, and compared with the BP neural network model, the comparative results show that the PSO_WNN forecasting model has higher predicted accuracy.

关键词： MBR Membrane flux particle swarm optimization algorithm Wavelet neural network BP neural network

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Application of PSO algorithm with Dynamic Inertia Weight in Medical Image Thresholding Segmentation 19

Application of PSO algorithm with Dynamic Inertia Weight in ...

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19th Annual IEEE International Conference on e-Health Networking, Applications and Services (Healthcom)

作者： LiuNa JiangYan LiShu Hubei Univ Med Ctr Hlth Adm & Dev Studies Shiyan Peoples R China

ISBN: (纸本)9781509067046

Medical image segmentation is an important application in medical image processing. In order to improve the efficiency of Medical image segmentation, a method to select optimal threshold by PSO algorithm with Dynamic Inertia Weight(DW-PSO) is proposed. It makes the proportion of the local and global searching ability can be effectively controlled in the whole process of optimal searching. Compared with PSO algorithm, DW-PSO gets better effects of image segmentation. The data of experiment shows that DW-PSO has obvious advantage in computing efficiency, the precision and stability of searching optimal threshold, as well as the effect of image segmentation.

关键词： particle swarm optimization algorithm Inertia weight Optimal threshold Medical image segmentation

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Intelligent Sign Language Recognition Using Enhanced Fourier Descriptor: A Case of Hausa Sign Language 2

Intelligent Sign Language Recognition Using Enhanced Fourier...

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2nd IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Hassan, S. T. Abolarinwa, J. A. Alenoghena, C. O. Bala, S. A. David, M. Farzaminia, Ali Fed Univ Technol Minna Dept Telecommun Engn Minna Nigeria Univ Malaysia Sabah Fac Engn Kota Kinabalu Malaysia

ISBN: (纸本)9781538608463

Hausa sign language (HSL) is the main communication medium among deaf-mute Hausas in northern Nigeria. HSL is so unique that a deaf-mute individual from other part of the country can rarely understand it. HSL includes static and dynamic hand gesture recognitions. In this paper we present an intelligent recognition of static, manual and non-manual HSL using an enhanced Fourier descriptor. A Red Green Blue (RGB) digital camera was used for image acquisition and Fourier descriptor was used for features extraction. The features extracted chosen manually and fed into artificial neural network (ANN) which was used for classification. Thereafter particle swarm optimization algorithm (PSO) was used to optimize the features based on their fitness in order to obtain high recognition accuracy. The optimized features selected gave a higher recognition accuracy of 90.5% compared to the manually selected features that gave 74.8% accuracy. High average recognition accuracy was achieved;hence, intelligent recognition of HSL was successful.

关键词： Hausa Sign Language Fourier Descriptor particle swarm optimization algorithm Artificial Neural Network

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Dimensional Synthesis of a Four-bar Linkage Mechanism via a PSO-based Cooperative Neural Network Approach 25

Dimensional Synthesis of a Four-bar Linkage Mechanism via a ...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Asaeikheybari, Golnoush Lafmejanit, Amir Salimi Kalhorj, Ahmad Masouleh, Mehdi Tale Shahid Beheshti Univ Dept Elect Engn Tehran Iran Univ Tehran Fac New Sci & Technol Human & Robot Interact Lab Tehran Iran Univ Tehran Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran Univ Tehran Human & Robot Interact Lab Sch Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781509059638

The dimensional synthesis problem is one of the challenging problems in robotics which has initiated several mathematical challenges. In this paper, a novel algorithm is proposed based on combination of particle swarm optimization (PSO) and Cooperative Neural Network (CNN) for solving synthesis problem of a four-bar linkage which leads to an optimization problem. The cooperative network, so-called PS-CNN, consists of memory-retaining particles which collaborate together based on PSO algorithm in a cooperative interaction converge to the optimal dimensional synthesis solution. In the complete-connected network, each neuron provides a solution. Thereby, solutions are updated according to the neurons' memory, their interaction with other neurons and the global best solution of the neurons in order to provide a proper solution to the optimization problem. The objective of the optimization problem is to minimize the distance of the robot's end-effector from the 5 prescribed points by the user when traversing them. Simulation results reveal the desirable performance of the PS-CNN for robot synthesis with higher complexities. Furthermore, the proposed approach opens an avenue to extend it.

关键词： Interactive Neural Network Four-bar Linkage particle swarm optimization algorithm Robot Synthesis

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Binary Cat swarm optimization for Cryptanalysis 11

Binary Cat Swarm Optimization for Cryptanalysis

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IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

作者： Amic, Seeven Soyjaudah, K. M. Sunjiv Ramsawock, Gianeshwar Univ Mascareignes Dept Software Engn Pamplemousses Mauritius Univ Mauritius Dept Elect & Elect Engn Reduit Mauritius

ISBN: (纸本)9781538623473

This research paper investigates the relevance of the Cat swarm optimization (CSO) algorithm to cryptanalysis. It is a relatively new evolutionary metaheuristic technique to solve those problems which belong to NP-hard class based on the swarm intelligence of felids, commonly known as cats. Cryptanalysis is an active field of experimental research as it can consolidate or depreciate the viability of modern ciphers. To the extent of our knowledge, there is no evidence that the CSO has ever been applied to the cryptanalysis problem. Experimental outcomes demonstrate that the Binary Cat swarm optimization (BCSO) algorithm is efficient for the cryptanalysis of the Data Encryption Standard (DES) using chosen plaintext attack. Further, it produces optimal keys with higher fitness as compared to particle swarm optimization. This paper shows that BCSO can be successfully utilized for the cryptanalysis of block ciphers with promising outcomes.

关键词： Cryptanalysis DES swarm intelligence Binary Cat swarm optimization algorithm particle swarm optimization algorithm Fitness Function

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Biomechanical study on the nonlinear stiffness behavior of the calf muscle and ground reaction force estimation during running 5

Biomechanical study on the nonlinear stiffness behavior of t...

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5th RSI International Conference on Robotics and Mechatronics (IcRoM)

作者： Jalali, Peyman Varahram, Sina Hassannejad, Reza Ettefagh, Mir Mohammad Univ Tabriz Dept Mech Engn Tabriz Iran

ISBN: (纸本)9781538657034

Understanding the biomechanical behavior of the human body in different conditions of locomotion can be very advantageous for many purposes, such as developing the humanoid robots. With this approach, the aim of this study is investigating the nonlinear stiffness behavior of the calf muscle and the exerted ground reaction force (GRF) during running. For this purpose, a new active model of human body is proposed, which the distinctive features of this model are as follows: 1) The simulation model is considered to be five degrees of freedom (5-DOF) by using the standard solid model to simulate the calf muscle;2) The optimum values of the considered mechanical parameters are obtained using the particle swarm optimization (PSO) algorithm;3) The stiffness of the calf muscle is considered to be nonlinear and active, which is appropriately formulated with regard to the viscoelasticity nature of the calf muscle. This model is tested for two types of hard and soft shoes, and validated by the results of previous studies. Then the effects of different conditions of body mass distribution, touchdown velocities, and different types of shoes on the GRF are investigated and discussed. The results of this study can be used in producing and development of humanoid robots, artificial limbs, etc.

关键词： Calf muscle Ground Reaction Force Active Mass-Spring-Damper model particle swarm optimization algorithm

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SVM Classification: optimization with the SMOTE algorithm for the Class Imbalance Problem 6

SVM Classification: Optimization with the SMOTE Algorithm fo...

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6th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781509067428

In this paper a new approach to selection of the optimal parameters values for the SMOTE (Synthetic Minority Over-sampling Technique) algorithm in the problem of the SVM (Support Vector Machine) classification of imbalanced datasets has been suggested. This approach allows reducing the time expenditures for the search of the optimum parameters values of the SMOTE algorithm. The experimental results show that the offered approach allows increasing the classification quality of the SVM classifier.

关键词： sampling imbalanced dataset SVM classifier particle swarm optimization algorithm hybridization

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Research on Path Planning of Automatic Handling Robot 2

Research on Path Planning of Automatic Handling Robot

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2nd International Conference on Information Technology and Intelligent Transportation Systems (ITITS)

作者： Hou, Yuanbin He, Liu Song, Chunfeng Zhang, Yanyan Li, Dong Xian Univ Sci & Technol Sch Elect & Control Engn Yanta Rd 58 Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781614997856;9781614997849

Aiming at the problem that path planning for automatic handling robot in an environment with obstacles, the working environment model of the handling robot is analyzed. And then a path optimization algorithm based on fusing ant colony and particle swarm optimization algorithm is proposed. First of all, this algorithm uses the global search ability of particle swarm to go on a rough search and quickly plans the starting point to the end of the initial path. Then, the pheromone distribution is performed on the initial path. Finally, the ant colony algorithm is used to search the path carefully to get the optimal path. Experimental verification shows that compared with a single ant colony or particle swarm optimization algorithm, fused algorithm about ant colony and particle swarm optimization has a significant improvement in the number of iterations and path planning.

关键词： Handling robot path planning particle swarm optimization algorithm ant colony algorithm

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Optimal Design of Fractional Order Fuzzy PID Controller with Simultaneous Auto-tuned Fuzzy Control Rules and Membership Functions 2

Optimal Design of Fractional Order Fuzzy PID Controller with...

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2nd Conference on swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Chaleshtori, Hengameh Nouraei Mohammadi, Seyed Mohammad Ali Bijami, Ehsan Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9781509043309

This paper proposes a new intelligent particle swarm optimization (PSO) based method for design of optimal fractional order fuzzy PID (FOFPID) controller with simultaneous auto-tuned fuzzy control rules and membership functions. In the proposed method the parameters of FOFPID controller including input scaling factors, output scaling factors, fractional order of derivative and integrator, fuzzy rule base and membership functions are considered as tuning parameters and optimized simultaneously using PSO algorithm. Moreover, to reduce the fuzzy system design effort and computational complexity, a novel simultaneous tuning approach is proposed for determining the membership functions and fuzzy rule base. The newly suggested design approach provides a flexible controller with simple structure and straightforward algorithm. To evaluate the effectiveness of the proposed method, the proposed FOFPID controller is applied to solve the Load Frequency Control (LFC) problem in a representative power system with considerations governor saturation and the results are compared to the one obtained by a FOFPID controller with fixed fuzzy part and a fractional order PID (FOPID) controller. Simulation results indicate the superiority of proposed method.

关键词： Fractional order fuzzy PID controller Load frequency control particle swarm optimization algorithm

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Optimal Placement of Fault Indicators in Distribution System Using PSO algorithm 43

Optimal Placement of Fault Indicators in Distribution System...

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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Li, Weijian Chen, Wei Guo, Chuangxin Jin, Yu Gong, Xiao Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China China Southern Power Grid Guizhou Power Grid Guiyang Power Supply Bur Guiyang Guizhou Peoples R China

ISBN: (纸本)9781538611272

Recently, fault indicators with communication function have been increasingly used in fault diagnosis of distribution networks. In this paper, an objective function which considers the reliability index of the distribution network and economic factors synthetically has been proposed for optimal placement of fault indicators in distribution networks, and binary particle swarm optimization algorithm is used for solving the optimization problem. The validity of the proposed model is verified by testing on the IEEE-33 node distribution system. The test results show that there is no need to install too many fault indicators in distribution networks and reasonable choice of the number and location of the fault indicator can achieve the comprehensive optimization of distribution network reliability and economy. At the same time, the results and processes of optimization are compared with those obtained by immune algorithm, the result shows that binary particle swarm optimization algorithm is better because of its fast convergence speed and less iteration times, which is an advantage for solving this optimal allocation problem.

关键词： Fault Indicators Optimal Placement particle swarm optimization algorithm

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