Design of double fuzzy clustering-driven context neural networks

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

doi:10.1016/j.neunet.2018.03.018

Cited by 18 publications

(2 citation statements)

References 29 publications

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“…Recently, type-1/2 fuzzy set theory has been applied to various control system fields such as fuzzy networked singularly perturbed systems, fuzzy passive filters, and T-S fuzzy Markov jump chaotic systems [40], [41], [42]. To use FNNs for real-world problems, Oh and Pedrycz proposed a variety of fuzzy rule-based neural networks combined with clustering, optimization, and dimensionality reduction [10], [11], [12], [13], [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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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Section: Introductionmentioning

confidence: 99%

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

2023

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“…There are several methods for generating and learning fuzzy classification rules from the data space, including simple heuristic procedures, fuzzy neural technology [4], clustering methods [5] and genetic algorithms [6], among which heuristic and meta-heuristic Algorithms include particle swarm optimization [7], simulated annealing [8], firefly [9], artificial bee colony optimization [10] and a series of biological and physical systems formed by behaviour. In addition, in the above algorithms, the fine-tuning of the parameters will affect the convergence speed of the optimization process [12].…”

Section: Introductionmentioning

confidence: 99%

Brain Storm Optimization Algorithm for Data Classification

Zhang

Zhu

et al. 2021

Preprint

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With the increasing of the number of dimensions or variables in the search space, the inductive learning of fuzzy rule classifier will be influenced by the generation and optimization of rules. Thus, the extensibility and accuracy of fuzzy systems will be affected. In this paper, the brain storm optimization algorithm was used. A new fuzzy system was designed by modifying the rules definition process in traditional fuzzy system. In the derivation of rules, the exponential model was introduced to improve the traditional brain storming algorithm. On the basis, this new fuzzy system was used for the research on data classification. The experimental results show that this new fuzzy system can improve the accuracy of data classification.

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