Design of electronic cam for lower hook mechanism of fishing net-weaving machine based on polynomial fitting

2022 ◽  
pp. 004051752110687
Author(s):  
Cankun Ming ◽  
Xinfu Chi ◽  
Zhijun Sun ◽  
Yize Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Spline Interpolation ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Motion Path ◽  
Polynomial Fitting ◽  
Swarm Optimization ◽  
B Spline ◽  
Fitting Method ◽  
Quintic Polynomial

The working efficiency and stability of the double hook-based fishing net-weaving machine is mainly determined by the lower hook mechanism. In this work, a new kind of lower hook mechanism, which is driven by four servo motors, is presented, and the electronic cam curve of the lower hook mechanism is introduced. First, cubic B-spline interpolation is used to get the basic motion path of the lower hook plate, and then the piecewise quintic polynomial fitting method is used to fit the motion path. Finally, self-adaptive mutation-based particle swarm optimization is put forward and used to obtain the optimal parameters of the quintic polynomial, which performs better compared with the other two particle swarm optimization algorithms in this study. Experiments suggest that the electronic cam curve generated by the piecewise quintic polynomial fitting has got 55.91% (horizontal motors) and 60.96% (vertical motors) optimization in maximum motor torque compared with curves generated by cubic B-spline interpolations. In addition, the new lower hook mechanism and its moving curve described in this paper improved the theoretical weaving speed of the fishing net-weaving machine, providing a basis for digital improvement of the knotted net-weaving industry.

Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3804-3808
Author(s):  
Zhi Mei Duan ◽  
Jia Tang Cheng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Bp Network ◽  
Swarm Optimization ◽  
Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

Action recognition based on adaptive mutation particle swarm optimization for SVM

2017 ◽  
Vol 25 (6) ◽  
pp. 1669-1678 ◽  
Author(s):  
张国梁 ZHANG Guo-liang ◽  
贾松敏 JIA Song-min ◽  
张祥银 ZHANG Xiang-yin ◽  
徐 涛 XU Tao
Keyword(s):  
Particle Swarm Optimization ◽  
Action Recognition ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Swarm Optimization

scholarly journals Globally-optimal prediction-based adaptive mutation particle swarm optimization

2017 ◽  
Vol 418-419 ◽  
pp. 186-217 ◽  
Author(s):  
Quanlong Cui ◽  
Qiuying Li ◽  
Gaoyang Li ◽  
Zhengguang Li ◽  
Xiaosong Han ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Optimal Prediction ◽  
Swarm Optimization

A Hybrid Forecasting Model for Non-Stationary Time Series

2012 ◽  
Vol 3 (2) ◽  
pp. 67-82 ◽  
Author(s):  
Yi Xiao ◽  
Jin Xiao ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stationary Time Series ◽  
Adaptive Mutation ◽  
Forecasting Model ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Individual Fitness ◽  
Hybrid Forecasting

In time series analysis, an important problem is how to extract the information hidden in the non-stationary and noise data and combine it into a model for forecasting. In this paper, the authors propose a TEI@I based hybrid forecasting model. A novel feed forward neural network is developed based on the improved particle swarm optimization with adaptive genetic operator (IPSO-FNN) for forecasting. In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Subsequently, a crossover rate which only depends on generation and an adaptive mutation rate based on individual fitness are designed. The parameters of FNN are optimized by binary and decimal particle swarm optimization. Further, the forecast results of IPSO-FNN are adjusted with the knowledge from text mining and an expert system. The empirical results on the container throughput forecast of Tianjin Port show that forecasts with the proposed method are much better than some other methods.

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