scholarly journals Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Zhangang Hao ◽  
Hongwei Ge ◽  
Tianpeng Gu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Annotation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Automatic Image Annotation ◽  
Swarm Optimization ◽  
Support Vector Clustering ◽  
Vector Clustering ◽  
Two Stages

With the progress of network technology, there are more and more digital images of the internet. But most images are not semantically marked, which makes it difficult to retrieve and use. In this paper, a new algorithm is proposed to automatically annotate images based on particle swarm optimization (PSO) and support vector clustering (SVC). The algorithm includes two stages: firstly, PSO algorithm is used to optimize SVC; secondly, the trained SVC algorithm is used to annotate the image automatically. In the experiment, three datasets are used to evaluate the algorithm, and the results show the effectiveness of the algorithm.

An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

2016 ◽  
Vol 25 (8) ◽  
pp. 1248-1258 ◽  
Author(s):  
Fayçal Megri ◽  
Ahmed Cherif Megri ◽  
Riadh Djabri
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Comfort ◽  
Support Vector Regression ◽  
Thermal Sensation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Statistical Regression ◽  
Swarm Optimization ◽  
Comfort Indices

The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET*), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.

scholarly journals Forecasting Initial Public Offering Pricing Using Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) In Iran

2017 ◽  
Vol 7 (1) ◽  
pp. 336 ◽  
Author(s):  
Shaho Heidari Gandoman ◽  
Navab Kiamehr ◽  
Mahmood Hemetfar
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Initial Public Offering ◽  
Stock Exchange ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Benefits ◽  
Support Vector ◽  
Swarm Optimization ◽  
Public Offering

The present study compares the ability of neural networks, support vector machine, and model derived from combining particles swarm optimization (PSO) algorithm and support vector machine (SVM) to forecast the initial public offering pricing. The purpose of this research is to design a model that helps investors recognize the validity of the initial public offering pricing and hunt profitable opportunities. The variables used in this study are selected among those variables which are in the disposal of investors who have limited access to information before the offering. On the other hand, these results can be useful for publishing companies, admissions consultant, underwriting and legislators of the stock exchange. We have considered the ninth day offering prices, since volatilities are gone and prices seem to be more realistic. The results show that the combination of particle swarm optimization (PSO) algorithm and support vector machine (SVM) markedly increases the forecasting power. As a result, support vector machine models can increase the accuracy of initial public offering pricing and provide significant economic benefits as reducing less than real pricing costs.

Nonlinear Inertia Weigh Particle Swarm Optimization Combines Simulated Annealing Algorithm and Application in Function and SVM Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3467-3471 ◽  
Author(s):  
Bin Jiao ◽  
Zhi Xiang Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Local Equilibrium ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Performance ◽  
Support Vector ◽  
Swarm Optimization ◽  
Searching Ability

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.

CREDIT SCORING USING MULTI-KERNEL SUPPORT VECTOR MACHINE AND CHAOS PARTICLE SWARM OPTIMIZATION

2012 ◽  
Vol 11 (03) ◽  
pp. 1250019 ◽  
Author(s):  
YUN LING ◽  
QIUYAN CAO ◽  
HUA ZHANG
Keyword(s):  
Particle Swarm Optimization ◽  
Kernel Function ◽  
Credit Scoring ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Swarm Optimization

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.

scholarly journals Damage Identification of Bridge Based on Modal Flexibility and Neural Network Improved by Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hanbing Liu ◽  
Gang Song ◽  
Yubo Jiao ◽  
Peng Zhang ◽  
Xianqiang Wang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Damage Identification ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Bp Network ◽  
Swarm Optimization ◽  
Modal Flexibility ◽  
Two Stages

An approach to identify damage of bridge utilizing modal flexibility and neural network optimized by particle swarm optimization (PSO) is presented. The method consists of two stages; modal flexibility indices are applied to damage localizing and neural network optimized by PSO is used to identify the damage severity. Numerical simulation of simply supported bridge is presented to demonstrate feasibility of the proposed method, while comparative analysis with traditional BP network is for its superiority. The results indicate that curvature of flexibility changes can identify damages with both single and multiple locations. The optimization of bias and weight for neural network by fitness function of PSO algorithm can realize favorable damage severity identification and possesses more satisfactory accuracy than traditional BP network.

scholarly journals A Novel Hybrid Model for Predicting Blast-Induced Ground Vibration Based on k-Nearest Neighbors and Particle Swarm Optimization

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xuan-Nam Bui ◽  
Pirat Jaroonpattanapong ◽  
Hoang Nguyen ◽  
Quang-Hieu Tran ◽  
Nguyen Quoc Long
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Ground Vibration ◽  
Nearest Neighbors ◽  
Pso Algorithm ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Swarm Optimization ◽  
Surface Mines ◽  
A New Technique

Abstract In this scientific report, a new technique of artificial intelligence which is based on k-nearest neighbors (KNN) and particle swarm optimization (PSO), named as PSO-KNN, was developed and proposed for estimating blast-induced ground vibration (PPV). In the proposed PSO-KNN, the hyper-parameters of the KNN were searched and optimized by the PSO. Accordingly, three forms of kernel function of the KNN were used, Quartic (Q), Tri weight (T), and Cosine (C), which result in three models and abbreviated as PSO-KNN-Q, PSO-KNN-T, and PSO-KNN-C models. The valid of the proposed models was surveyed through comparing with those of benchmarks, random forest (RF), support vector regression (SVR), and an empirical technique. A total of 152 blasting events were recorded and analyzed for this aim. Herein, maximum explosive per blast delay (W) and the distance of PPV measurement (R), were used as the two input parameters for predicting PPV. RMSE, R2, and MAE were utilized as performance indicators for evaluating the models’ accuracy. The outcomes instruct that the PSO algorithm significantly improved the efficiency of the PSO-KNN-Q, PSO-KNN-T, and PSO-KNN-C models. Compared to the three benchmarks models (i.e., RF, SVR, and empirical), the PSO-KNN-T model (RMSE = 0.797, R2 = 0.977, and MAE = 0.385) performed better; therefore, it can be introduced as a powerful tool, which can be used in practical blasting for reducing unwanted elements induced by PPV in surface mines.

Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

2011 ◽  
Vol 121-126 ◽  
pp. 2809-2813
Author(s):  
Dong Wang ◽  
Xin Qing Wang ◽  
Xiao Long Wang ◽  
Sheng Liang ◽  
Yang Zhao
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Fuel System ◽  
Swarm Optimization ◽  
Pressure Signal ◽  
Svm Model

In order to overcome the difficulty in selecting parameters of support vector machine (SVM) when modeling the PT fuel system fault diagnosis, SVM optimized by particle swarm optimization (PSO) algorithm was proposed. The PSO-SVM model was established and the fault multi-classifiers of the SVM were got. The pressure signal of the PT fuel inlet and outlet at different rotational speed and conditions was collected. The algorithm of PSO-SVM was used to train and recognize the pressure signal. The result of experiment confirms the validity of this method through comparison of the BP-NN, SVM and the PSO-SVM.

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