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

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Association Rules Mining Based On Improved PSO algorithm 2

Association Rules Mining Based On Improved PSO Algorithm

引用

2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)

作者： Shang Qianxiang Wu Ping East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200062 Peoples R China

ISBN: (纸本)9781538620304

One of the most classic algorithms for association rules mining is the Apriori algorithm. But it can't satisfy the requirement as the increasing scale of the data. It has some disadvantages such as scanning database too many times, setting support and confidence thresholds artificially. particle swarm optimization is one of the classic heuristic algorithms and some researchers has used it to association rules mining. But the problem that it may fall into the local optimal solution prematurely affects the efficiency of the algorithm. A new improved particle swarm optimization algorithm is proposed to solve this problem by controlling the particle velocity. In order to improve the efficiency and reliability of the algorithm in the condition of guarantee the global searching capability, an adaptive acceleration coefficient control method based on distance is used.

关键词： association rules Apriori algorithm particle swarm optimization algorithm acceleration coefficients

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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...

引用

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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A Novel Inventory and Distribution Planning Model with Non-conforming Items Disposal Under Fuzzy Random Environment 10th

A Novel Inventory and Distribution Planning Model with Non-c...

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10th International Conference on Management Science and Engineering Management (ICMSEM)

作者： Kang, Kai Pu, Wei Ma, Yanfang Wei, Xuguang Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China

ISBN: (纸本)9789811018374;9789811018367

This paper investigates a novel inventory and distribution planning model with non-conforming items disposal (NIDPNCID) under fuzzy random environment to minimize the whole process cost. In this process, a certain fraction or a random number of produced items are defective. These non-conforming items are rejected in order to improve the consumer satisfaction. To solve the problem, a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm with fuzzy random simulation is proposed, which can be easy to implement. In more specific terms, DP-based PSO can reduce the dimensions of a particle by using the state equation, which significantly reduced the solution space.

关键词： Inventory Non-conforming items Distribution Fuzzy random particle swarm optimization algorithm

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Classifying DNA Microarray for Cancer Diagnosis via Method Based on Complex Networks 1

引用

13th International Conference on Intelligent Computing (ICIC)

作者： Wu, Peng Dong, Likai Fan, Yuling Wang, Dong Univ Jinan Sch Informat Sci & Engn Jinan Shandong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China

ISBN: (数字)9783319633121

ISBN: (纸本)9783319633121;9783319633114

Performing microarray expression data classification can improve the accuracy of a cancer diagnosis. The varying technique including Support Vector Machines (SVMs), Neuro-Fuzzy models (NF), K-Nearest Neighbor (KNN), Neural Network (NN), and etc. have been applied to analyze microarray expression data. In this investigation, a novel complex network classifier is proposed to do such thing. To build the complex network classifier, we tried a hybrid method based on the particle swarm optimization algorithm (PSO) and Genetic Programming (GP), of which GP aims at finding an optimal structure and PSO accomplishes the fine tuning of the parameters encoded in the proposed classifier. The experimental results conducted on Leukemia and Colon data sets are comparable to the state-of-the-art outcomes.

关键词： DNA microarray data Complex network classifier particle swarm optimization algorithm Genetic Programming

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CT to Ultrasound Registration for Non-Invasive Kinematic Analysis of Knee Joints 4

CT to Ultrasound Registration for Non-Invasive Kinematic Ana...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Li, Meng Huang, Qinghua Li, Wenrui Li, Jianyi South China Univ Technol Dept Elect & Informat Engn Guangzhou Guangdong Peoples R China Southern Med Univ Dept Anat Guangdong Prov Key Lab Med Biomech Sch Basic Med Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538611074

Accurate measurement of the motion of individual bones in the knee joint is extremely important for the recovery of patients with motor dysfunction. The most recognized clinical method for measuring is inserting tantalum beads into the bone act as landmark prior to imaging using X-ray equipment at present. Nevertheless, the approach is invasive and the exposure to ionizing radiation is inevitable. This paper puts forward a new non-invasive, precise 3D kinematic analysis system based on registration of CT-scans and B-mode ultrasonic images which is able to capture the flexion of the bones in the knee joint accurately. The experimental results in this paper indicate that the system is capable of measuring the motion of the bones in the knee joint with high precision which is able to meet the need of 3D kinematic analysis for the knee joint.

关键词： CT-US Registration H-shaped image non-invasive particle swarm optimization algorithm mutual information

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particle swarm algorithm Based Task Scheduling for Many-Core Systems 12

Particle Swarm Algorithm Based Task Scheduling for Many-Core...

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12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Lu Junliang Hu Wei Shen Huan Li Yaxin Liu Jing Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China

ISBN: (纸本)9781509061617

Task scheduling is one of the key factors that determine the performance of the computer systems. When there are more cores on a single chip, how to schedule the multiple tasks to these cores is still important and also a challenge. In order to optimize the efficiency of task scheduling of many-core computer systems, this paper proposes a new task scheduling algorithm based on particle swarm ant colony algorithm. In this algorithm, the fitness function is chosen according to the system model with many cores and multiple tasks. This algorithm uses B-level scheduling method in the initial stage to generate the initial pheromone distribution. In the iteration process, the improved way is used to update pheromone to adjust operator. At the same time, the crossover and mutation strategy of genetic algorithm is used for adaptive crossover and mutation of local optimum and global optimum particles, which makes the location of particles be able to change and update according to the fitness value of particles. The experimental results show that the improved algorithm is superior to the genetic algorithm in the performance of task scheduling.

关键词： task scheduling particle swarm optimization algorithm many-core

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Handwritten Numeral Recognition with a Quantum Neural Network Model 2

Handwritten Numeral Recognition with a Quantum Neural Networ...

引用

2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Mao Yaxuan Kazuyuki Aihara Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo

ISBN: (纸本)9781509064151;9781509064144

Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks(QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Backpropagation(BP) algorithm and particle swarm optimization(PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.

关键词： Handwritten numeral recognition Quantum neural network Back-propagation algorithm particle swarm optimization algorithm

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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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Two-Level Intellectual Classifier Based on the SVM algorithm 6

Two-Level Intellectual Classifier Based on the SVM Algorithm

引用

6th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Sokolova, Yulia Ryazan State Radio Engn Univ Gagarin Str 59-1 Ryazan 390005 Russia

ISBN: (纸本)9781509067428

In this paper the two-level intellectual classifier based on the SVM algorithm has been offered. This classifier works as the group of the SVM classifiers at the first level and as the final SVM classifier on the base of the modified PSO algorithm at the second level. Herewith, we use the original experimental dataset to develop the different SVM classifiers at the first level, and the new dataset, containing only supports vectors of the SVM classifiers of the first level, to develop the final SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered two-level intellectual classifier.

关键词： SVM classifier support vectors kernel function type kernel function parameters regularization parameter 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...

引用

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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