Fuzzy control based on genetic algorithm in intelligent psychology teaching system

2021 ◽  
pp. 1-9
Author(s):  
Xiaojia Pang ◽  
Yuwen Ning
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Control Theory ◽  
Classroom Teaching ◽  
Improved Genetic Algorithm ◽  
Group Function ◽  
Teaching Process ◽  
Teaching Management ◽  
Teaching System ◽  
Volume Group

The advancement of science has made computer technology and the education industry more and more closely related, and the development of intelligent teaching systems has also opened a new path for classroom teaching. This paper studies the application of fuzzy control based on genetic algorithms in the intelligent psychology teaching system. Facing the complicated variables in the teaching process, the improved genetic algorithm can better realize dynamic teaching decisions through fuzzy control. This article aims to improve the quality of psychology classroom teaching, and develops an intelligent psychology teaching system based on the fuzzy control theory of genetic algorithm. Combined with the current development of fuzzy control theory, the problems existing in the intelligent teaching system are studied and analyzed, and they have been optimized and improved. This paper proposes a control algorithm based on a teaching management system. The algorithm can implement fuzzy control on student models, knowledge organization structure, intelligent test papers and teaching decision-making. While restoring the real teaching process, it can better realize teaching students in accordance with their aptitude and improve teaching. The intelligence of the system. According to the system test data, the proportions of the difficulty of the system’s automatic test paper are 30.1%, 51.6%, 18.3%, which are in line with the designer’s set expectation of 3 : 5:2, which shows the improved genetic algorithm. It can realize the intelligent volume group function very well.

Word order detection in English classroom teaching based on improved genetic algorithm of block coding

2020 ◽  
pp. 1-12
Author(s):  
Deng Bowen
Keyword(s):  
Genetic Algorithm ◽  
Speech Recognition ◽  
Speech Enhancement ◽  
Word Order ◽  
Recognition System ◽  
Classroom Teaching ◽  
Speech Recognition System ◽  
Improved Genetic Algorithm ◽  
Block Coding ◽  
English Classroom

The performance of the speech recognition system for English classroom teaching is largely affected by the surrounding environment. These interference signals will seriously reduce the quality and intelligibility of the speech signal, thereby greatly reducing the performance of the far-field speech recognition system. Aiming at word order detection in English classroom teaching, this paper proposes an analysis model based on block coding and improved genetic algorithm. Moreover, for DNN-based single-channel speech enhancement algorithms, this paper proposes PDNNs and PLSTMs to solve the problem of serious performance degradation of prototype DNN speech enhancement under low signal-to-noise ratio. This method decomposes the entire enhancement task into multiple subtasks to complete, and the previously completed subtasks will provide prior knowledge for the subsequent subtasks, so that the subsequent subtasks can learn its goals better. In general, the experimental results prove the reliability of the model constructed in this paper.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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