AFCGD: an adaptive fuzzy classifier based on gradient descent

Shahparast, Homeira; Mansoori, Eghbal G.; Jahromi, Mansoor Zolghadri

doi:10.1007/s00500-018-3485-2

Cited by 9 publications

(1 citation statement)

References 41 publications

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“…This approach of structure identification requires the collecting of data in advance, which makes it inappropriate for use in real-time structure identification applications. In a number of studies, researchers have concentrated on the modeling of dynamic systems using fuzzy neural networks [8] Furthermore, the Bayesian TSK fuzzy model suggested in [9][10] without the requirement for previous expert knowledge, it is possible to specify the number of fuzzy rules. In order to prevent singularity, the error function's form has been modified to include the reciprocals of Gaussian membership function widths as independent variables, rather than only the widths of the functions themselves.…”

Section: Introductionmentioning

confidence: 99%

Learning Fuzzy Neural Networks by Using Improved Conjugate Gradient Techniques

Khudhur

Abbo²

2022

SinkrOn

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One of the optimal approaches for learning a Takagi Sugeno-based fuzzy neural network model is the conjugate gradient method proposed in this research. For the PRP and the LS approaches, a novel algorithm based on the Liu-Storey (LS) approach is created to overcome the slow convergence. The developed method becomes descent and convergence by assuming some hypothesis. The numerical results show that the developed method for classifying data is more efficient than the other methods, as shown in Table (2), where the new method outperforms the others in terms of average training time, average training accuracy, average test accuracy, average training MSE, and average test MSE.

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Section: Introductionmentioning

confidence: 99%