Reinforced hybrid interval fuzzy neural networks architecture: Design and analysis

Kim, Eun-Hu; Oh, Sung‐Kwun; Pedrycz, Witold

doi:10.1016/j.neucom.2018.04.003

Cited by 14 publications

(4 citation statements)

References 35 publications

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“…On the other hand, in the process of intelligent classroom design, there are many differences in its internal relevance [ 18 ]. Therefore, there are obvious differences in the degree of intelligence, and the application of the PCA-NN algorithm is even less [ 19 ]. In this context, it is of great significance to study the design of mathematics teaching intelligent classroom based on the PCA-NN algorithm [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

Zhang

2021

Computational Intelligence and Neuroscience

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With the increasing importance of mathematics in basic education, how to evaluate and analyze the intelligent effect of mathematics teaching classroom through scientific methods has become one of the indicators to evaluate the intelligent classroom. This paper studies the design and application of mathematics teaching intelligent classroom based on the PCA-NN (principal component analysis-neural network) algorithm. Firstly, this paper briefly describes the current research status of mathematics teaching intelligent classroom design and PCA-NN algorithm. Secondly, combined with the key factors of mathematics teaching, it formulates specific standards and puts forward an adaptive strategy of intelligent and personalized intelligent mathematics teaching classroom. Finally, the algorithm is verified by experiments. The results show that, for students with different mathematics basic levels, the mathematics teaching intelligent classroom based on the PCA-NN algorithm can effectively improve the quality of mathematics classroom teaching. Through the research on the factors such as teaching quality, effect, and interaction mode involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting teaching quality are determined. This paper analyzes and studies the system from different angles. The research results provide some help for the current quality evaluation of classroom teaching and use the PCA-NN algorithm to make quantitative analysis and multivariate verification of mathematics classroom teaching effect.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

Zhang

2021

Computational Intelligence and Neuroscience

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“…To make up defects of the ridgelet neural network, it should be improved based on advanced method. The fuzzy neural network is the comprehensive system of fuzzy logic and neural network, which has clear physical meaning and fast convergence . The fuzzy neural network can use neural network to achieve the fuzzy reasoning and use the weights of neural network to express fuzzy reasoning parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fuzzy neural network is the comprehensive system of fuzzy logic and neural network, which has clear physical meaning and fast convergence. 10 The fuzzy neural network can use neural network to achieve the fuzzy reasoning and use the weights of neural network to express fuzzy reasoning parameters. It set the initial value of neural network close to the global extreme point, and the learning performance of neural network can be improved.…”

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confidence: 99%

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Zhao

Ren

Gao

et al. 2019

Quality & Reliability Eng

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The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.

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“…In addition, the type-2 TSK fuzzy logic system (FLS) only uses BP-based learning to update the consequent parameters (coefficients). Nonetheless, the advantages of type-2 fuzzy sets, which deal more effectively with the uncertainty associated with given problems, may countervail these drawbacks [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Noise-Robust Fuzzy Classifier Designed With the Aid of Type-2 Fuzzy Clustering and Enhanced Learning

2023

Self Cite

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This paper introduces the design methodology of a noise-robust fuzzy classifier based on type-2 fuzzy clustering and enhanced learning methods. The design procedure for the noise-robust fuzzy classifier (NrFC) can be divided into two parts. First, interval type-2 fuzzy c-means clustering is applied to the hidden layer to minimize the effect of noise or outliers when training the model. Second, an enhanced learning method is employed to train the connection weights between the hidden and output layers. The proposed NrFC uses a cross-entropy error function as its cost function. The Softmax function represents a categorical distribution located at the output layer nodes. In addition, the connection weights of the output layer are adjusted through nonlinear least squares-based learning, and L 2 norm-regularization is considered to avoid the degradation of the generalization ability caused by overfitting. The learning mechanism is realized by adding the L 2 penalty term to the cross-entropy error function. It is used to cope with overfitting and multicollinearity problems, which generally appear in conventional fuzzy neural networks. The design methodology of the NrFC is discussed and analyzed using several publicly available benchmark datasets. The performance of the proposed networks is quantified through comprehensive experiments and comparative analysis.INDEX TERMS Interval type-2 fuzzy C-means, L 2 -norm regularization, multicollinearity, nonlinear least square, noise-robust fuzzy classifier.

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Reinforced hybrid interval fuzzy neural networks architecture: Design and analysis

Cited by 14 publications

References 35 publications

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Noise-Robust Fuzzy Classifier Designed With the Aid of Type-2 Fuzzy Clustering and Enhanced Learning

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