Classroom education effect evaluation model based on MFO intelligent optimization algorithm

2020 ◽  
pp. 1-12
Author(s):  
Ouyang Weimin

In order to improve the evaluation effect of classroom education, this paper proposes the MFO intelligent optimization algorithm based on the idea of machine learning, and builds the classroom education effect evaluation model based on the MFO intelligent optimization algorithm. Moreover, this paper uses a logarithmic spiral to simulate the path of the moth to the flame and invert the pending parameters in the mathematical model, and adds vertical and horizontal algorithms and chaos operators on this basis. The crisscross algorithm allows different moth individuals and the same moth to perform cross calculations with different computing dimensions to increase the diversity of moth populations, so that moths in the search space can traverse the entire search space as much as possible to find a better solution. Moreover, in view of the problems of BP neural network such as low fitting accuracy, this paper applies the CCMFO algorithm to improve the BP neural network to form the CCMFO-BP algorithm, and improves the weight and threshold update process of the BP neural network to make the network operation more efficient and accurate. Finally, this paper designs experiments to analyze the performance of the model constructed in this paper. The research results show that the model constructed in this paper meets the expected requirements.

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4937-4952
Author(s):  
Qing Wu ◽  
Jie Wang ◽  
Gang Xu ◽  
Shuai Li ◽  
Dechao Chen

Traditional back-propagation (BP) neural networks can implement complex nonlinear mapping relationships, and solve internal mechanism problems. However, as number of samples increases, training BP neural networks may consume a lot of time. For this reason, to improve the efficiency as well as prediction accuracy of the neural network model, in this paper, we propose an intelligent optimization algorithm, by leveraging the beetle antennae search (BAS) strategy to optimize the weights of neural network model, and apply it to the population prediction. A series of experiments demonstrate the improved accuracy of the proposed algorithm over BP neural networks. In particular, the calculation time spent of neural network model via the proposed algorithm is only 20% of the one of BP neural network model. Finally, we present a reasonable trend of population growth in China, and analyze the causes of changes in population trends, which may provide an effective basis for the department to adjust population development strategies


Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.


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