Application of optimized Kalman filtering in target tracking based on improved Gray Wolf algorithm.

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect.

Quality and Process Optimization of Infrared Combined Hot Air Drying of Yam Slices Based on BP Neural Network and Gray Wolf Algorithm.

In this paper, the effects on drying time (Y1), the color difference (Y2), unit energy consumption (Y3), polysaccharide content (Y4), rehydration ratio (Y5), and allantoin content (Y6) of yam slices were investigated under different drying temperatures (50-70 °C), slice thicknesses (2-10 mm), and radiation distances (80-160 mm). The optimal drying conditions were determined by applying the BP neural network wolf algorithm (GWO) model based on response surface methodology (RMS). All the above indices were significantly affected by drying conditions (p < 0.05). The drying rate and effective water diffusion coefficient of yam slices accelerated with increasing temperature and decreasing slice thickness and radiation distance. The selection of lower temperature and slice thickness helped reduce the energy consumption and color difference. The polysaccharide content increased and then decreased with drying temperature, slice thickness, and radiation distance, and it was highest at 60 °C, 6 mm, and 120 mm. At 60 °C, lower slice thickness and radiation distance favored the retention of allantoin content. Under the given constraints (minimization of drying time, unit energy consumption, color difference, and maximization of rehydration ratio, polysaccharide content, and allantoin content), BP-GWO was found to have higher coefficients of determination (R2 = 0.9919 to 0.9983) and lower RMSEs (reduced by 61.34% to 80.03%) than RMS. Multi-objective optimization of BP-GWO was carried out to obtain the optimal drying conditions, as follows: temperature 63.57 °C, slice thickness 4.27 mm, radiation distance 91.39 mm, corresponding to the optimal indices, as follows: Y1 = 133.71 min, Y2 = 7.26, Y3 = 8.54 kJ·h·kg-1, Y4 = 20.73 mg/g, Y5 = 2.84 kg/kg, and Y6 = 3.69 µg/g. In the experimental verification of the prediction results, the relative error between the actual and predicted values was less than 5%, proving the model's reliability for other materials in the drying technology process research to provide a reference.

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum.

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

Rapid determination of trace homogeneous catalyst in chemical production.

As one of the important factors in chemical production, catalyst content directly affects the process of reaction and the quality of products. The quantitative analysis of trace catalyst in homogeneous reaction system is still faced with great challenges. In this work, a simple and effective approach to the rapid determination of trace homogeneous catalyst (THC) was proposed based on UV-vis spectrophotometry. Wavelet transform and Tchebichef curve moment methods were combined with gray wolf algorithm to extract the feature information from the original UV-vis spectra of samples. Then the partial least-squares model was established. The predictive correlation coefficient (Rp2) was 0.9842, and the limit of quantification was 0.07 ‰. The intra-day and inter-day precision were 3.97 % and 4.36 %, respectively. The spiked recoveries of three different concentrations in actual samples were between 97.6 and 101.9 %. The results indicated that the obtained model was satisfactory and could be used in practical measurement. Compared with the conventional modeling methods, the proposed approach was more accurate and reliable, which provided a feasible new pathway for enterprise product quality control.

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning.

The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

The Bent-Tube Nozzle Optimization of Force-Spinning With the Gray Wolf Algorithm.

Force-spinning is a popular way to fabricate various fine fibers such as polymer and metal nanofibers, which are being widely employed in medical and industrial manufacture. The spinneret is the key of the device for spinning fibers, and the physical performance and morphology of the spun nanofibers are largely determined by its structure parameters. In this article, the effect of spinneret parameters on the outlet velocity is explored and the spinneret parameters are also optimized to obtain the maximum outlet velocity. The mathematical model of the solution flow in four areas is established at first, and the relationship between outlet velocity and structure parameters is acquired. This model can directly reflect the flow velocity of the solution in each area. Then, the optimal parameters of outlet diameter, bending angle, and curvature radius are obtained combined with the gray wolf algorithm (GWA). It is found that a curved-tube nozzle with a bending angle of 9.1°, nozzle diameter of 0.6 mm, and curvature radius of 10 mm can obtain the maximum outlet velocity and better velocity distribution. Subsequently, the simulation is utilized to analyze and compare the velocity situation of different parameters. Finally, the fiber of 5 wt% PEO solution is manufactured by a straight-tube nozzle and optimized bent-tube nozzle in the laboratory, and the morphology and diameter distribution were observed using a scanning electron microscope (SEM). The results showed that the outlet velocity was dramatically improved after the bent-tube parameters were optimized by GWA, and nanofibers of better surface quality could be obtained using optimized bent-tube nozzles.