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Yang-Yin Lin
Researcher at National Chiao Tung University
Publications - 22
Citations - 1032
Yang-Yin Lin is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Neuro-fuzzy & Fuzzy set operations. The author has an hindex of 12, co-authored 22 publications receiving 846 citations. Previous affiliations of Yang-Yin Lin include National Chung Hsing University & Chungshan Institute of Science and Technology.
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Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network
TL;DR: This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy Neural Network (IRSFNN), for prediction and identification of dynamic systems and compares it to other well-known recurrent FNNs.
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A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications
TL;DR: This paper proposes a Takagi-Sugeno-Kang (TSK)-type-based self-evolving compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN), which produces smaller root-mean-square errors and converges more quickly in system modeling and noise cancellation problems.
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Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network
TL;DR: A generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue is proposed.
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A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing
TL;DR: A recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing and is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions.
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Simplified Interval Type-2 Fuzzy Neural Networks
TL;DR: A simple interval type-2 FNN, which uses intervaltype-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang type in the consequent of the fuzzy rule, which yields fewer test errors and less computational complexity than other type-1 fuzzy systems.