Swarm Optimization Improved BP Algorithm for Microchannel Resistance Factor

Shen, Teng; Chang, Jiaqing; Liang, Zhongwei

doi:10.1109/access.2020.2969526

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…In recent years, scholars have extensively studied heuristic algorithms such as ant colony algorithms, particle swarm algorithms and genetic algorithms to replace BP neural networks [29], [30]. Genetic algorithms (GA) in intelligent algorithms have the characteristics searching for the best in the global range and fast search efficiency.…”

Section: B Ga-bp Algorithmmentioning

confidence: 99%

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Research and Embedded Implementation of Let-Off and Take-Up Dynamic Control Based on Fuzzy Neural Network and Vector Control Optimization

et al. 2022

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This paper investigates the warp let-off and take-up mechanism of rapier looms in order to solve the problem that the warp tension of rapier looms fluctuates greatly and the warp let-off is difficult to maintain constant. The design and hardware implementation of a let-off and take-up control system based on fuzzy neural network (FNN) and vector control (VC) are presented to improve the control level of warp tension and drive performance of the let-off and take-up system. Firstly, the spring-damper dynamic model of the warp is established according to the mechanical properties of the warp. The parametric expression of warp tension and the control strategy of fixed angle interval based on let-off and take-up motions are constructed according to the generation mechanism and fluctuation law of warp tension. Then, on the basis of fuzzy reasoning mechanism and neural network model, the fusion theory of fuzzy neural network is introduced, and a tension controller based on T-S fuzzy neural network (FNN) is designed. FNN is trained by introducing genetic optimization and the backpropagation fusion algorithm (GA-BP). In addition, a specialized let-off and take-up hardware circuit is constructed through embedded technology, and the SVPWM algorithm is used as the driving scheme of the hardware circuit. Finally, simulation and actual weaving experiments test the proposed let-off and take-up control system and hardware circuit. The results show that, when compared to PID and fuzzy PID, the proposed fuzzy neural network algorithm has higher tension control accuracy and can effectively restrain the rapier loom's warp tension undulation. The designed hardware circuit and SVPWM algorithm have the fast and stable driving ability, which ensures the constant let-off amount.INDEX TERMS Rapier loom, tension control, vector control, fuzzy neural network, genetic algorithm.

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Section: B Ga-bp Algorithmmentioning

confidence: 99%

“…The optimization goal of the fuzzy neural network is to make the actual output corresponding to the input data of the network approach the given output of the network to the maximum extent. Take the objective function as shown in formula (30) Transform the objective function to get the fitness function:…”

Section: B Ga-bp Algorithmmentioning

confidence: 99%

Research and Embedded Implementation of Let-Off and Take-Up Dynamic Control Based on Fuzzy Neural Network and Vector Control Optimization

et al. 2022

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“…Ren et al [18] developed a new technique for calculating the UBC of pile foundations by optimizing BPNN utilizing the adaptive genetic algorithm and the adaptive particle swarm optimization algorithm. Shen et al [19] proposed a new group optimization approach for microchannel resistance factor prediction by combining BP with the Particle Swarm Evolution (PSE) algorithm. Liu et al [20] combined the chaos optimization method and gradient descent method to create a novel search optimization method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

Xi,

Jin,

et al. 2024

Mathematics

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The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects.

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“…Compared with the traditional models, this model has a higher detection rate and a lower false alarm rate. Shen et al (2020) proposed a particle swarm evolution (PSE)-BP algorithm to predict microchannel resistance factors. Compared with the BP algorithm, the PSE-BP algorithm can dramatically improve ANN training efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Wind Power Prediction Method Based on DE-BP Neural Network

Wang

et al. 2022

Front. Energy Res.

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With the continuous increase of installed capacity of wind power, the influence of large-scale wind power integration on the power grid is becoming increasingly apparent. Ultra-short-term wind power prediction is conducive to the dispatching management of the power grid and improves the operating efficiency and economy of the power system. In order to overcome the intermittency and uncertainty of wind power generation, this article proposes the differential evolution–back propagation (DE-BP) algorithm to predict wind power and addresses such shortcomings of the BP neural network as its falling into local optimality and slow training speed when predicting. In this article, the DE algorithm is used to find the optimal value of the initial weight and threshold of the BP neural network, and the DE-BP neural network prediction model is obtained. According to the data of a wind farm in Northwest China, the short-term wind power is predicted. Compared with the application of the BP model in wind power prediction, the results show that the accuracy of the DE-BP algorithm is improved by about 5%; compared with the genetic algorithm–BP model, the prediction time is shortened by 23.1%.

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Swarm Optimization Improved BP Algorithm for Microchannel Resistance Factor

Cited by 8 publications

References 34 publications

Research and Embedded Implementation of Let-Off and Take-Up Dynamic Control Based on Fuzzy Neural Network and Vector Control Optimization

Research and Embedded Implementation of Let-Off and Take-Up Dynamic Control Based on Fuzzy Neural Network and Vector Control Optimization

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

A Wind Power Prediction Method Based on DE-BP Neural Network

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