Particle Swarm Optimization Support Vector Machine Model Research Articles

The TAM and PSO–SVM methods for assessing the crack damage of concrete slab

According to the crack images of concrete slab, two models – the Topographic Attentive Mapping (TAM) Neural Network which simulates the process of visual system and the Support Vector Machine (SVM) which is optimized by the Particle Swarm Optimization (PSO) algorithm – were adopted to assess the structural damages based on the digital image processing technology. Firstly, the TAM and the PSO–SVM assessment model was trained separately according to the crack images of bridge slab collected in advance and the damage level assessed by experts. Then, the established assessment models were used to assess the testing samples of the crack images. The results showed that the structural damage assessment method based on crack images is feasible and practical. The assessment effect of the TAM model is significantly better than that of the PSO–SVM model.

Real estate price forecasting based on SVM optimized by PSO

The real estate market has a close relationship with us. It plays a very important role in economic development and people's fundamental needs. So, accurately forecasting the future real estate prices is very significant. Support vector machine (SVM) is a novel type of learning machine which has been proved to be available in solving the problems of limited sample learning, nonlinear regression, as well as, better to overcome the “curse of dimensionality”. However, the selected parameters determine its learning and generalization. Thus, it is essential to determine the parameters of SVM. Compared to ant colony algorithm, grid algorithm, genetic algorithm, particle swarm optimization (PSO) is powerful and easy to implement. Therefore, in the study, real estate price forecasting by PSO and SVM is proposed in the paper, where PSO is chosen to determine the parameters of SVM. The real estate price forecasting cases are used to testify the forecasting performance of the proposed PSO–SVM model. The experimental results indicate that the proposed PSO–SVM model has good forecasting performance.

Improved annual rainfall-runoff forecasting using PSO–SVM model based on EEMD

Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation (R) and Nash–Sutcliffe efficiency (NSE). The PSO–SVM–EEMD model improved ANN model forecasting (65.99%) and OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively. Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station.

Using a hybrid evolution approach to forecast financial failures for Taiwan-listed companies

Bankruptcy has been an important topic in finance and accounting research for a long time. Recent major bankruptcies have included seemingly robust companies such as Enron, Kmart, Global Crossing, WorldCom, and Lehman Brothers. These cases have become of serious public concern due to the huge influence these companies have on the real economy. This research proposes a hybrid evolution approach to integrate particle swarm optimization (PSO) with the support vector machine (SVM) technique for the purpose of predicting financial failures. The preparation phase collected an initial sample of 68 companies listed by the Taiwan Stock Exchange Corporation (TSEC). The financial datasets were constructed based on 33 financial ratios, four non-financial ratios and one combined macroeconomic index. To select suitable indicators for the input vector, the principle component analysis (PCA) technique was applied to reduce the data and determine how groupings of indicators measure the same concept. In the swarming phase, PSO was applied to obtain suitable parameters for SVM modeling without reducing the classification accuracy rate. In the modeling phase, the SVM model was used to build a training set that was used to calculate the model's accuracy and fitness value. Finally, these optimized parameters were used in the hybrid PSO–SVM model to evaluate the model's predictive accuracy. This paper provides four critical contributions. (1) Using the PCA technique, the statistical results indicate that the financial prediction performance is mainly affected by financial ratios rather than non-financial and macroeconomic ratios. (2) Even with the input of nearly 70% fewer indicators, our approach is still able to provide highly accurate forecasts of financial bankruptcy. (3) The empirical results show that the PSO–SVM model provides better classification accuracy (i.e. normal vs. bankrupt) than the grid search (Grid–SVM) approach. (4) For six well-known UCI datasets, the PSO–SVM model also provides better prediction accuracy than the Grid–SVM, GA–SVM, SVM, SOM, and SVR–SOM approaches. Therefore, this paper proposes that the PSO–SVM approach is better suited for predicting potential financial distress.

Research on the mining roadway displacement forecasting based on support vector machine theory

In view of the difficulty in supporting the surrounding rocks of roadway 3–411 of Fucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was put forward based on particle swarm optimization. The kernel function and model parameters were optimized using particle swarm optimization. It is shown that the forecast result is very close to the real monitoring data. Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BP network model. The results show that PSO-SVM method is better in the aspect of prediction accuracy and the PSO-SVM roadway deformation pre-diction model is feasible for the large deformation prediction of coal mine roadway.

Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO–SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO–SVM model. The experimental results indicate that the PSO–SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample.