Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Abstract

Based on bottom-up fuzzy rough data analysis, a new rough neural network decision-making model is proposed. Through supervised Gaustafason-Kessel (G-K) clustering algorithm, proper fuzzy clusters are found to partition the input data space. At the same time cluster number is searched by monotone increasing process. If the cluster number matches with that exactly exist in data sets then excellent fuzzy rough data modeling (FRDM) model can be built. And by integrating it with neural network technique, corresponding rough neural network is constructed. Our method overcomes the defects of conventional top-down based rough logic neural network (RLNN) method, and it also achieves adaptive learning ability and comprehensive soft decision-making ability compared with FRDM model. The experiment results indicate that our method has stronger generalization ability and more compact network structure than conventional RLNN.

Prediction of Junior High School Students’ Problematic Internet Use: The Comparison of Neural Network Models and Linear Mixed Models in Longitudinal Study

“…Based on the comparison of the results and findings from this study, it is evident that the neural network model outperforms the linear mixed model when dealing with psychological longitudinal data. This conclusion aligns with 6,[41][42][43] which also demonstrated the superiority of neural network models in various predictive analyses. Specifically, Li found that the normalized mean square error of the neural network model was smaller than that of the linear mixed model for both long-term and short-term predictions on longitudinal data.…”

“…6 Zhang also highlighted the advantage of neural network models over linear mixed models in constructing disease-specific models, complementing traditional approaches. 41 Moreover, Wang et al compared neural network models and decision tree methods with linear regression models and observed that both neural network models and decision tree models exhibited high predictive power. 42 Similarly, Zhao et al conducted a cross-sectional study on PIU and time management tendencies among junior high school students using BP neural network models, and the findings indicated that neural network models offered more accurate predictions across different variables such as gender and regions.…”

Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

“…The Extreme learning machine also evolved from the feedforward neural network, which can randomize the input weights, bias, and the number of hidden layer neurons and then obtain the output weights by least squares without the need for the entire iteration of the network [ 34 , 35 ]. ELM is widely used in various fields such as pattern recognition, image processing, signal processing, combinatorial optimization, and prediction [ 66 , 67 , 68 , 69 ]. The structure of the ELM is shown in Figure 4 .…”