A novel adaptive feed-forward-PID controller of a SCARA parallel robot using pneumatic artificial muscle actuator based on neural network and modified differential evolution algorithm

2017 ◽  
Vol 96 ◽  
pp. 65-80 ◽  
Author(s):  
Nguyen Ngoc Son ◽  
Cao Van Kien ◽  
Ho Pham Huy Anh
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Pid Controller ◽  
Differential Evolution Algorithm ◽  
Artificial Muscle ◽  
Parallel Robot ◽  
Pneumatic Artificial Muscle ◽  
Feed Forward ◽  
Evolution Algorithm

scholarly journals FPGA–implementation of PID-controller by differential evolution optimization

2018 ◽  
Vol 8 (1) ◽  
pp. 395-402
Author(s):  
Mika Hanhila ◽  
Timo Mantere ◽  
Jarmo T. Alander
Keyword(s):  
Differential Evolution ◽  
Pid Controller ◽  
Differential Evolution Algorithm ◽  
Fpga Implementation ◽  
Solution Convergence ◽  
Fitness Value ◽  
Mutation Operation ◽  
Self Adaptation ◽  
Evolution Algorithm

Abstract We will describe an FPGA implementation of PID-controller that uses differential evolution to optimize the coefficients of the PID controller, which has been implemented in VHDL. The original differential evolution algorithm was improved by ranking based mutation operation and self-adaptation of mutation and crossover parameters. Ranking-based mutation operation improves the quality of solution, convergence rate and success of optimization. Due to the self-adaptive control parameters, the user does not have to estimate the mutation and crossover rates. Optimization have been performed by calculating for each generation fitness value by means of trial parameters. The final optimal parameters are selected based on the minimum fitness.

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

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