An Improved BP Neural Network Algorithm for the Evaluation System of Innovation and Entrepreneurship Education in Colleges and UniversitiesCHINAINNOVATIONS in higher educationBUSINESS formsALGORITHMSThe ability of college students has important index to evaluate the training quality of universities. Domestic ...
Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron (MLP) and back-propagation (BP) is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a ...
Keywords:imagecompression;BPnetwork;improvedgeneticalgorithm;GA-LMbpmethod 传统的图像压缩技术利用图像数据间冗余的 减少,用相应的编码技术对图像进行无损或有损的 压缩 [1] 。不同的压缩编码技术针对不同的图像数 据冗余,而近年来,随着人工神经网络技术的不断 发展,神经网络的一些良好特性:如并行性、非线 性、容...
So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR ...
The various examples above show that a combination algorithm can improve the prediction accuracy, and the prediction performance can be further enhanced if the parameters are optimized by an optimization algorithm. Therefore, this paper combines the improved sparrow search optimization algorithm (SCSSA) ...
However, the accuracy of these algorithms is not strong enough and the features of employee intention seldom work well in these models. To address this, our study aims to enhance the ability to forecast employee turnover and introduce a new method based on an improved random forest algorithm. ...
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Improved SLP method is used to build a comprehensive network; Secondly, a multi-objective mathematical model of functional area layout is built with the comprehensive goal of minimum logistics cost, energy consumption and environmental pollution. Genetic algorithm is introduced to obtain the optimal ...
The BP algorithm was utilised to train all neural networks. The prediction networks were trained with three ranges of ultimate load. The classifying network architecture was made of 14 inputs and 3 outputs parameters. The first group ranged from 30 to 120 kN, the second ranged from 80 to ...
The major drawback of back propagation (BP) learning algorithm e.g. very slow in training process, depends upon the initial parameters values by their possible chance neural network to get trapped in local minima. View article Read full article URL: https://www.sciencedirect.com/science/...