Wavelet Particle Swarm Optimization Research Articles

Wavelet decomposition-based multi-stage feature engineering and optimized ensemble classifier for stock market prediction

Expanded feature engineering to handle complex and noisy stock data is not much explored in financial forecasting. Along with feature engineering, hyperparameter tuning is also important. This paper provides a solution to handle these issues by elaborating on Discrete Wavelet Transform (DWT)-based feature engineering and hyperparameter-tuned ensemble model. The Multi-Stage Feature Engineering (MSFE) is proposed in which DWT-based decomposition is utilized to handle the noise. DWT decomposition expands the features; therefore, two-stage feature reduction is proposed in which first the filter method is used, and then the probabilistic method is utilized. Next, hyperparameter tuning of the ensemble model is offered through Particle Swarm Optimization (PSO). The proposed model is named as Wavelet-Particle Swarm Optimization (WPSO). WPSO is tested and evaluated on the three stock indices (NIFTY, NASDAQ, and NYSE), and provided 92.51%, 94.18%, and 87.62% accuracy for NIFTY, NASDAQ, and NYSE, respectively. The WPSO is validated by comparing it with state-of-the-art methods. The performance of the WPSO is statistically analyzed through the Bonferroni–Dunn post hoc test where WPSO positioned at rank 1 for all the evaluation metric and datasets. The WPSO empirically verifies that improving feature quality through MSFE and hyperparameter tuning of ensemble model significantly improves the predictive outcomes.

Research on Prediction Method of Distribution Network Activity in Green Electric Power through PSO-WNN

The convergence and prediction accuracy of the artificial neural network prediction model are affected by the initial parameter settings of the model, and the wavelet neural network (WNN) modeling algorithm can effectively overcome this defect and achieve better prediction results. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the initial weights, and the optimal value of the particle swarm algorithm is assigned to the WNN to replace the initial random assignment of the WNN. The WNN is trained according to the backpropagation (BP) algorithm until convergence. The particle swarm optimization wavelet neural network (PSO-WNN) model is used to predict the cost of a single grid activity based on the cost driver parameters of the power grid. The prediction error analysis shows that the model significantly improves the accuracy of the activity cost prediction, which is beneficial to the grid enterprise to effectively manage the cost drivers and track the specific effects of the grid activity cost which produced by the driver parameters.

Research on Damage Identification of Nonuniform Microcrack in Beam Structures

Nonuniform microcrack identification is of great significance in mechanical, aerospace, and civil engineering. In this study, the nonuniform crack is simplified as a semielliptical crack, and simplified calculation methods are proposed for damage severity and damage identification of semielliptical cracks. The proposed methods are based on the calculation method for uniform cracks. The wavelet transform and the intelligent algorithm (IA) are used to identify the damage location and the damage severity of the structure, respectively. The singularity of the wavelet coefficient can be used to identify the signal singularity quickly and accurately, and IA efficiently and accurately calculates the structural damage severity. The particle swarm optimization (PSO) algorithm and the genetic algorithm (GA), widely used, are applied to identify the damage severity of the beam. Numerical simulations and experimental analyses of beams with transfixion and semielliptical cracks are carried out to evaluate the accuracy of the semielliptical crack calculation method and the method of wavelet analysis combined with PSO and GA for nonuniform crack identification. The results show that the wavelet-particle swarm optimization (WPSO) and the wavelet-genetic algorithm (WGA) can accurately and efficiently identify the structural semielliptical damage location and severity and that these methods are not easily influenced by noise. The damage severity calculation method for semielliptical cracks can accurately calculate the semielliptical size and can be used to identify damage in beams with semielliptical cracks.

Research on Denoising Method of Metal Magnetic Memory Signal

As the main transportation mode of oil and gas, oil and gas pipelines play an irreplaceable role in energy transportation. Metal magnetic memory detection technology can detect early stress concentration and invisible damage, and can be detected under the action of the geomagnetic field, without the need to magnetize the pipeline in advance. Since the magnetic memory signal is relatively weak, the actual detected signal will be affected by environmental noise, sensor jitter, and pipeline surface deposits. Therefore, the magnetic memory signal needs to be denoised. In this paper, the translation invariant wavelet denoising method, which is improved based on wavelet threshold denoising method, is used to denoise the collected pipeline magnetic memory signals. The experimental results show that the signal-to-noise ratio (SNR) obtained by this method is 4.97 % higher than the unmodified wavelet threshold denoising, and 3.18 % higher than the SNR obtained by the particle swarm optimization wavelet threshold denoising.

Signal extraction using complementary ensemble empirical mode in pipeline magnetic flux leakage nondestructive evaluation.

The magnetic flux leakage (MFL) evaluation is often used for the overhauling of oil extracting operation in the oil field to realize the real-time damage assessment of the pipeline. Since the MFL signal is affected by various noise sources in the field, this paper introduces the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On the basis of this, a particle swarm optimization wavelet threshold (PSO-WT) method is proposed, and the signal reconstruction option is improved to extract the leakage magnetic flux signal of tubing defects. First, CEEMDAN is used to add pairs of positive and negative white noise to the MFL signal, and then the signal is decomposed into several intrinsic mode functions (IMFs). Second, the correlation coefficient selection limit is defined. Taking into account the characteristics of the decomposed signal, the useless IMFs and useful IMFs are selected from the IMF components, where some of the useful IMF components contain less noise. Third, the PSO-WT algorithm is combined to further filter the noisy and useful IMF components. Finally, the filtered IMF components and the pure useful IMF components are selected to reconstruct the signal. In the experiment, the ensemble empirical mode decomposition (EEMD) method and CEEMDAN are used to decompose the noisy MFL signals ensemble in the field. The MFL signal is reconstructed under the correlation coefficient selection. It can be seen from the comparison of EEMD that the MFL signal is reconstructed under the same conditions after CEEMDAN decomposition, and its signal-to-noise ratio is increased by 8%. At the same time, after CEEMDAN decomposition, the selected noisy useful IMFs are further filtered by the wavelet threshold (WT) method and the PSO-WT method. Also, it indicates that the reconstructed signal processed by PSO-WT is 17% higher than the reconstructed signal after WT processing.

Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network

The gear cracks of gear box are one of most common failure forms affecting gear shaft drive. It has become significant for practice and economy to diagnose the situation of gearbox rapidly and accurately. The extracted signal is filtered first to eliminate noise, which is pretreated for the diagnostic classification based on the particle filter of radial basis function. As traditional error back-propagation of wavelet neural network with falling into local minimum easily, slow convergence speed and other shortcomings, the particle swarm optimization algorithm is proposed in this paper. This particle swarm algorithm that optimizes the weight values of wavelet neural network (scale factor) and threshold value (the translation factor) was developed to reduce the iteration times and improve the convergence precision and rapidity so that the various parameters of wavelet neural network can be chosen adaptively. Experimental results demonstrate that the proposed method can accurately and quickly identify the damage situation of the gear crack, which is more robust than traditional back-propagation algorithm. It provides guidances and references for the maintenance of the gear drive system schemes.

Electric load simulator system control based on adaptive particle swarm optimization wavelet neural network with double sliding modes

In this article, an adaptive particle swarm optimization wavelet neural network with double sliding modes controller is proposed to address the complex nonlinearities and uncertainties in the electric load simulator. The adaptive double sliding modes–particle swarm optimization wavelet neural network algorithm with the self-learning structures and parameters is designed as a torque tracking controller, in which a number of hidden nodes are added and pruned by the structure learning algorithm, and the parameters are online adjusted by the adaptive particle swarm optimization at the same time. Moreover, one conventional sliding mode is introduced to track the time-varying reference command, and the other complementary sliding mode is adopted to attenuate the effect of the approximation error. Furthermore, the relative parameters should comply with some estimation laws on the basis of the Lyapunov theory used to guarantee the system stability. Finally, the simulation experiments are carried out on the hardware-in-the-loop platform for the electric load simulator, the performance of the adaptive double sliding modes–particle swarm optimization wavelet neural network with structure learning is verified compared with some similar control methods. In addition, different amplitudes and frequencies of the reference commands are introduced to further evaluate the effectiveness and robustness of the proposed algorithms.

A Mathematical Modelling Evaluation of Wushu Training Using Multi-Agent System and Wavelet Particle Swarm Optimization

A Mathematical Modelling Evaluation of Wushu Training Using Multi-Agent System and Wavelet Particle Swarm Optimization

A hybrid Wavelet Neural Network and Switching Particle Swarm Optimization algorithm for face direction recognition

Face direction recognition is an important research issue in human–computer interaction. In order to improve recognition accuracy, a novel hybrid approach called Switching Particle Swarm Optimization–Wavelet Neural Network (SPSO–WNN) is presented. In this model, we employ the recently proposed Switching Particle Swarm Optimization (SPSO) algorithm to optimize the parameters of weights, scale factors, translation factors and threshold in Wavelet Neural Network (WNN). The proposed SPSO–WNN method has fast convergence speed and higher learning ability than conventional WNNs. Especially, a mode-dependent velocity updating equation with Markovian switching parameters is introduced in SPSO to overcome the contradiction between the local search and the global search, which makes it easy to jump the local minimum. The experiment results of the recognition for face direction show the feasibility and effectiveness of the proposed method. Compared with Particle Swarm Optimization–Wavelet Neural Network (PSO–WNN), Genetic Algorithm–Wavelet Neural Network (GA–WNN) and WNN, the proposed method has much better performance over them.

A Combined Particle Swarm Optimization-Wavelet Transform Based Strategy for Power Management of PEM Fuel Cell Powered Hybrid System

This paper proposed a combined particle swarm optimization-wavelet-transform based power management for PEM fuel cell powered hybrid electric vehicles which including a PEM fuel cell, a lithium-ion battery and an ultra-capacitor. This combined particle swarm optimization-wavelet-transform based frequency decoupling power management strategy is capable of identifying the high frequency of the ultra-capacitor, the PEM fuel cell low frequency and the lithium-ion battery medium frequency power demand of the hybrid systems. The hybrid system efficiency and life cycle can be extended. We made simulation by using the SimpowerSystem of MATLAB, the results shows the combined particle swarm optimization-wavelet transform based strategy for power management is validated.

Modified method for wavelet neural network model

To improve the performance of Wavelet Neural Network( WNN) model in dealing with complex nonlinear problems, concerning the shortcomings of premature convergence, poor late diversity, poor search accuracy of Quantumbehaved Particle Swarm Optimization( QPSO) algorithm, a modified quantum-behaved particle swarm algorithm was proposed for WNN training by introducing weighting coefficients, introducing Cauchy random number, improving contraction-expansion coefficient and introducing natural selection at the same time. And then, it replaced the gradient descent method with the modified quantum-behaved particle swarm algorithm, trained the wavelet coefficients and network weights, and then input the optimized combination of parameters into wavelet neural network to achieve the algorithm coupling. The simulation results on three UCI standard datasets show that the running time of the Modified Quantum-behaved Particle Swarm Optimization-Wavelet Neural Network( MQPSO-WNN) was reduced by 11% ~ 43%, while the calculation error was decreased by 8% ~ 57%,compared with wavelet neural network, PSO-WNN and QPSO-WNN. Therefore, the MQPSO-WNN model can approximate the optimal value more quickly and more accurately.

An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization

Wind speed series show unsteady and nonlinear phenomena. The accurate forecast of wind speed is important for the safety of renewable energy utilization. Compared to the prediction models which use single algorithms, hybrid models always have higher accuracy. Based on the theories of Wavelet, classical time series analysis, genetic algorithm, particle swarm optimization and artificial neural networks, two hybrid forecasting frameworks [the Wavelet-Genetic Algorithm (GA)-Multilayer Perceptron (MLP) and the Wavelet-Particle Swarm Optimization (PSO)-Multilayer Perceptron (MLP)] are proposed to predict non-stationary wind speeds. Comparisons of forecasting performance using different algorithm combinations are provided to investigate the contribution of different components in those two hybrid frameworks. The results based on three experimental cases show that: (1) both of the two proposed hybrid forecasting frameworks are suitable for the diverse accuracy requirements in wind speed predictions, which can be applied to wind power systems; and (2) in both of the two hybrid frameworks, the contribution of the GA and the PSO components in improving the MLP are not statistically significant while that of the Wavelet component is statistically significant.

An Obstacle Recognition Method Based on Improved PSO-WNN for Deicing Robot on Voltage Transmission Line

An intelligent obstacle recognition method based on improved particle swarm optimization wavelet neural network (IPSO-WNN) for deicing robot on voltage transmission line is presented because traditional obstacle recognition methods often have errors due to structural constraints. Firstly, the improved PSO algorithm is proposed by introduce idea of probabilistic leap in simulated annealing. Secondly , the improved algorithm is used to optimize the parameters of the wavelet network. Finally, the trained IPSO-WNN is used to recognize and classify the wavelet moments of the obstacle edged images of robot. Experimental results show that compared with PSO-WNN this method have quicker convergence rate and higher classification precision and improve obviously recognition function of obstacle on transmission line.

A Multi-Degree-of-Freedom System Modal Parameters Wavelet Transform Identification Method

It is precondition for realizing the mechanical dynamic optimization design and the dynamic modification to analyze the characteristic of multi-degree-of-freedom system. The paper discusses a kind of modal parameters wavelet transform identification method based on Modified Particle Swarm Optimization (PSO) algorithm, furthermore, proposes the PSO wavelet ridge extraction algorithm, and gives the formulas identifying modal parameters from a wavelet ridge. The proposed method applies to the dynamic characteristic identification of a flow meter to explore the effectiveness. The results prove the method is precision and insensitive to noise.

Study of Fault Classification Based on PSO Wavelet Neural Network

In this paper, a novel studying-training algorithm of wavelet neural-network based on particle swarm optimization (PSO) was presented. Then it was compared with the traditional gradient descent algorithm by the fault classification experiment. The simulation result get a conclusion that the wavelet neural-network trained by PSO not only reduces the iterations, but also get the better convergence precision. It is further indicated that PSO algorithm is fit to train the wavelet neural-network and the optimized network has good signal classification ability.

A System Characteristic Parameters Identification Method by Wavelet Transform Image Ridge

Dynamic characteristic of a system have to be obtained so as to offer the reliable data for the system dynamic modification and dynamic optimum design. The system modal parameters identification can be changed into the global optimization problem in wavelet plane because the wavelet ridges can carry much information of system characteristic parameters. Firstly, the paper will introduce the so-called PSO (Particle Swarm Optimization) technique into the modal analysis and propose the PSO wavelet ridge extraction algorithm. Furthermore, the formulas identifying modal parameters from a wavelet ridge are derived and computation steps were given. The simulation experiments are implemented in order to evaluate precision of this method and the robustness of noise. Finally, the proposed method applies to the modal parameters identification of a watermelon to explore the effectiveness. The experiment results prove the method is precision and insensitive to noise.