Nonsingular Gradient Descent Algorithm for Interval Type-2 Fuzzy Neural Network

Interval type-2 fuzzy neural network (IT2FNN) is widely used to model nonlinear systems. Unfortunately, the gradient descent-based IT2FNN with uncertain variances always suffers from low convergence speed due to its inherent singularity. To cope with this problem, a nonsingular gradient descent algo...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 35; číslo 6; s. 8176 - 8189
Hlavní autori:	Han, Honggui, Sun, Chenxuan, Wu, Xiaolong, Yang, Hongyan, Qiao, Junfei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial neural networks Convergence Convergence speed Fuzzy logic Fuzzy neural networks interval type-2 fuzzy neural network (IT2FNN) Learning systems Machine learning Neural networks Neurons Nonlinear systems nonsingular gradient descent algorithm (NSGDA) Optimization Oscillators Parameters root inverse variance (RIV) singularity Training
ISSN:	2162-237X, 2162-2388, 2162-2388
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