Research on Power Consumption Model of Permanent Magnet Direct Drive Belt Conveyor System Based on Fruit Fly Optimization Algorithm-Generalized Regression Neural Network-Particle Swarm Optimization

2021 ◽  
Vol 16 (6) ◽  
pp. 941-948
Author(s):  
Gui-Mei Wang ◽  
Li-Chen Zhang ◽  
Jie-Hui Liu ◽  
Li-Jie Yang
Keyword(s):  
Power Consumption ◽  
Permanent Magnet ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Direct Drive ◽  
Fruit Fly Optimization ◽  
Consumption Model ◽  
Generalized Regression

The permanent magnet direct drive electric drum is used to replace the driving device of the traditional belt conveyor, which simplifies the structure. However, the permanent magnet direct drive electric drum still operates in a high speed and stable state after starting, and cannot transport materials reasonably, belt speed cannot match the coal quantity reasonably; the problem of energy waste is still severe. Thus, based on fruit fly optimization algorithm-generalized regression neural network-particle swarm optimization algorithm, the power consumption network model of the permanent magnet direct drive belt conveyor system is established. The relationship among belt velocity, coal transport quantity and power is obtained, and the optimal belt velocity is found with the power consumption network model. Thus, the minimum power is obtained. The algorithm selects the optimal smoothing factor by fruit fly optimization algorithm, inputs the optimal smoothing factor into generalized regression neural network and establishes the optimal power consumption model. Then establishes the matching relationship of coal quantity, optimal power, and optimal belt speed in the power consumption model by the adaptive weight particle swarm optimization. The model is compared with the network model with smoothing factors of 0.7 and 0.4. The comparisons show that the optimized model performs better, which can be better applied to establish the energy consumption model of the system.

scholarly journals A new optimization particle filtering navigation location method for aquatic plants cleaning workboat in crab farming

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878791 ◽  
Author(s):  
Chengzhi Ruan ◽  
Dean Zhao ◽  
Shihong Ding ◽  
Yueping Sun ◽  
Jinhui Rao ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Aquatic Plants ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Network Methods ◽  
Generalized Regression

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the meanR of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network- particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the meanT of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat.

Prediction of microfibril angle in Dahurian larch wood using visible–near infrared spectroscopy and chemometric techniques

2019 ◽  
Vol 27 (4) ◽  
pp. 270-277
Author(s):  
Ying Li ◽  
Brian K Via ◽  
Qingzheng Cheng ◽  
Yaoxiang Li
Keyword(s):  
Neural Network ◽  
Stepwise Regression ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Coupled Model ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Generalized Regression

The microfibril angle of the S2 layer in the secondary cell wall of the tracheid is important for molecular and microscopic properties that influence collapse resistance, longitudinal modulus of elasticity and other lateral properties of conifers at the macroscopic level. This research aimed to investigate the feasibility of using a fruit fly optimization algorithm for visible and near infrared modeling optimization of Dahurian larch wood microfibril angle prediction. Originally, the linear relationship between microfibril angle and their raw spectra and visible and near infrared spectra pretreated by wavelet transform was established. Then, a nonlinear coupled model was built by combining the stepwise regression analysis and generalized regression neural network methods. As a final point, fruit fly optimization algorithm was used for optimizing stepwise regression analysis–generalized regression neural network coupled model. It was found that stepwise regression analysis–generalized regression neural network coupled model coupled model based on the optimization of fruit fly optimization algorithm simplify visible and near infrared spectral data and its prediction results ([Formula: see text] = 0.90, RMSEP = 0.75, mean average percentage error ([Formula: see text]) = 0.05) outperforms original partial least squares model ([Formula: see text] = 0.86, RMSEP = 0.88, [Formula: see text] = 0.06). This work demonstrated the feasibility of using improved chemometric techniques for improving the precision of visible and near infrared spectra in the prediction of microfibril angle.

scholarly journals Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

2021 ◽  
Vol 45 (2) ◽  
pp. 296-300
Author(s):  
M. Liu ◽  
Z.H. Sun
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Target Recognition ◽  
Recognition Rate ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Radar Target ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Radar Target Recognition

With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise interference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

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