The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability. 

Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming

A novel webpage layout aesthetic evaluation model for quantifying webpage layout design

Given the significance of cultivating students’ autonomous learning ability, there is a need to develop an instructional model that can improve students’ awareness and behavior of autonomous learning, as well as to explore the effectiveness and optimization of this model effectively. Taking college English course as a case study, this paper constructs a blended learning mode based on SPOC, which combines advantages of online and offline teaching. 15 types of nonredundant sets resulting from 500 questionnaires has been explored, and the optimal factor combinations have been found out from 15 types with the technology of data mining to optimize the mode constructed previously. Optimized blended learning mode, emphasizing the optimal factors more, has been applied to College English curriculum design and teaching practice in China. Surveys of students’ achievement and autonomous learning behavior have been conducted after experiment. The results of the research indicate that the optimized blended learning mode will stimulate foreign language learners’ learning motivation, cultivate their autonomous learning ability, so as to construct and improve their autonomous learning behavior further.

https://journals.sagepub.com/doi/pdf/10.1177/21582440211071108

Improvement of Students’ Autonomous Learning Behavior by Optimizing Foreign Language Blended Learning Mode

Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

/pdf/a-new-view-of-multisensor-data-fusion-research-on-2p0006k7vg.pdf

A New View of Multisensor Data Fusion: Research on Generalized Fusion

With the advent of big data and the information age, the data magnitude of various complex networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary networks, while hypergraphs have the ability to describe and characterize higher order relationships, which have attracted extensive attention from academia and industry in recent years. Firstly, this paper described the development process, the application areas, and the existing review research of hypergraphs; secondly, introduced the theory of hypergraphs briefly; then, compared the learning methods of ordinary graphs and hypergraphs from three aspects: matrix decomposition, random walk, and deep learning; next, introduced the structural optimization of hypergraphs from three perspectives: dynamic hypergraphs, hyperedge weight optimization, and multimodal hypergraph generation; after that, the applicability of three uncertain hypergraph models were analyzed based on three uncertainty theories: probability theory, fuzzy set, and rough set; finally, the future research directions of hypergraphs and uncertain hypergraphs were prospected.

https://www.mdpi.com/2227-7390/10/11/1921/pdf?version=1654574166

Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods

Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey

In practical applications, imbalanced data has brought great challenges to classification problems. In this paper, we propose two new methods: (1) a new oversampling method, named Tomek-CASUWO, to address the issue of class imbalance; (2) a new classifier, named ILS-RBFNN, to increase the accuracy of prediction of customer consumption behavior. Since current ASUWO algorithm is easily affected by noise samples and fuzzy class boundaries, we propose Tomek-CASUWO to address these problems: (i) the Tomek links algorithm is used to filter noise samples; (ii) CASUWO is used to avoid overlapping class boundaries; (iii) Tomek-CASUWO is used to synthesize new samples. Also, we propose a new classifier based on RBFNN, named ILS-RBFNN, to improve the prediction accuracy: (i) the hybrid kernel is developed by combining Gaussian and Polynomial; (ii) an Immune Algorithm (IA) is used to optimize the centers of RBFNN; (iii) Least-Squares (LS) is used to calculate the biases and weights. Wine-consumer behavior data is used to compare our Tomek-CASUWO with other oversampling methods. We compare ILS-RBFNN with several well-known kernel functions and parameter update methods. The experimental results show that Tomek-CASUWO can significantly improve the prediction accuracy of a classifier, and ILS-RBFNN outperforms other classification methods. We also conduct experiments on the extended real-world dataset. Finally, the robustness and applicability of ILS-RBFNN are verified on the eleven UCI datasets. All results show that the proposed two methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of ILS-RBFNN for predicting customer behavior. 

Xiaoyun Jia

Papers

A novel webpage layout aesthetic evaluation model for quantifying webpage layout design

Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey

A new oversampling method and improved radial basis function classifier for customer consumption behavior prediction

Adversarial multi-task learning with inverse mapping for speech enhancement