Swarm Optimization-based Support Vector Machine Research Articles

A Differential Particle Swarm Optimization-based Support Vector Machine Classifier for Fault Diagnosis in Power Distribution Systems

This paper proposes a new differential particle swarm optimization (DPSO) method for obtaining optimum support vector machine (SVM) parameters used for electrical fault diagnosis in ra ...

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R2) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value.

A new sense-through-foliage target recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine

Sense-through-foliage target detection and recognition is of interest to both military and civilian research. In this paper, a new recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine (SVM) is proposed to recognize targets obscured by foliage. To seek the optimal parameters of SVM, a new hybrid differential evolution and self-adaptive particle swarm optimization (DEPSO) algorithm is developed to determine the optimal parameters for SVM with the highest accuracy and generalization ability. In this work, sparse representation is applied to extract the target features from real target echo waveforms measured by a bistatic ultra-wideband (UWB) radar system. Then, the extracted features are input into the proposed method to automatically recognize the types of targets. This method is validated by experiments taken in the forest environment. Compared with the commonly used particle swarm optimization-optimized SVM (PSO-SVM), SVM, k-nearest neighbor (KNN) and BP neural network (BPNN), the proposed DEPSO-SVM can achieve a higher accuracy. Experimental results demonstrate the effectiveness and robustness of the proposed method for sense-through-foliage target recognition.

Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.

A novel sense-through-foliage target recognition system based on sparse representation and improved particle swarm optimization-based support vector machine

A new recognition system of improved particle swarm optimization-based support vector machine (SVM) combined with sparse representation-based feature extraction is proposed for recognize targets obscured by foliage. Real data sets of four kinds of samples are acquired using a bistatic ultra-wideband (UWB) radar system. Sparse representation (SR) theory is applied to analyzing the components of received target echo signals and sparse coefficients are used to describe target features, the dimension of the sparse coefficients is reduced using principal component analysis (PCA). Support vector machine is a powerful tool for solving the recognition problem with small sampling, nonlinearity and high dimension. Improved particle swarm optimization (IPSO) is developed in this study to determine the optimal parameters for SVM with the highest accuracy and generalization ability. The experiment results indicate that the method of feature extraction using SR can effectively represent the original data better. The recognition result of the proposed method is also compared with SVM, k-nearest neighbor (KNN) and BP neural network (BPNN). The effectiveness of the proposed approach is verified by experiments taken in the forest environment. Our findings show that the proposed method combined with bistatic UWB radar technology provides a good access to achieve the aim of automatic sense through foliage target recognition.

Hybrid of artificial immune system and particle swarm optimization-based support vector machine for Radio Frequency Identification-based positioning system

This study intends to propose a hybrid of artificial immune system (AIS) and particle swarm optimization (PSO)-based support vector machine (SVM) (HIP–SVM) for optimizing SVM parameters, and applied it to radio frequency identification (RFID)-based positioning system. In order to evaluate HIP–SVM’s capability, six benchmark data sets, Australian, Heart disease, Iris, Ionosphere, Sonar and Vowel, were employed. The computational results showed that HIP–SVM has better performance than AIS-based SVM and PSO-based SVM. HIP–SVM was also applied to classify RSSI for indoor positioning. The experiment results indicated that HIP–SVM can achieve highest accuracy compared to those of AIS–SVM and PSO–SVM. It demonstrated that RFID can be used for storing information and in indoor positioning without additional cost.

Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine

Diagnosis of arrhythmia cordis is very significant to ensure human health and save human lives. Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linear and high dimension. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. Thus, in the study, the proposed PSO-SVM model is applied to diagnosis of arrhythmia cordis, in which PSO is used to determine free parameters of support vector machine. The experimental data from MIT-BIH ECG database are used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve higher diagnostic accuracy than artificial neural network in diagnosis of arrhythmia cordis.

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.

Chinese Grain Production Forecasting Method Based on Particle Swarm Optimization-based Support Vector Machine

Forecasting of grain production is an important resource for establishing agriculture policy. Particle swarm optimization-based support vector machine (PSO-SVM) is applied to forecast grain production in this paper. In PSOSVM model, particle swarm optimization (PSO) is used to determine free parameters of support vector machine. PSO is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. The Chinese grain production 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 in Chinese grain production forecasting. Consequently, PSO-SVM is a proper method in Chinese grain production forecasting.