Improving the interpretability of TSK fuzzy models by combining global learning and local learning

1998 ◽  
Vol 6 (4) ◽  
pp. 530-537 ◽  
Author(s):  
J. Yen ◽  
Liang Wang ◽  
C.W. Gillespie
Keyword(s):  
Local Learning ◽  
Global Learning ◽  
Fuzzy Models

Water level forecasting using neuro-fuzzy models with local learning

2016 ◽  
Vol 30 (6) ◽  
pp. 1877-1887 ◽  
Author(s):  
Phuoc Khac-Tien Nguyen ◽  
Lloyd Hock-Chye Chua ◽  
Amin Talei ◽  
Quek Hiok Chai
Keyword(s):  
Water Level ◽  
Local Learning ◽  
Fuzzy Models ◽  
Neuro Fuzzy

scholarly journals Brain-inspired global-local learning incorporated with neuromorphic computing

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Yujie Wu ◽  
Rong Zhao ◽  
Jun Zhu ◽  
Feng Chen ◽  
Mingkun Xu ◽  
...  
Keyword(s):  
Great Promise ◽  
Local Learning ◽  
Global Learning ◽  
Neuronal Dynamics ◽  
Neuromorphic Computing ◽  
Vision Sensors ◽  
Learning Capabilities ◽  
Meta Learning ◽  
Learning Scenarios ◽  
Many Core

AbstractThere are two principle approaches for learning in artificial intelligence: error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs to fully exploit its advantages. Here, we present a neuromorphic global-local synergic learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It can meta-learn local plasticity and receive top-down supervision information for multiscale learning. We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors. It achieves significantly higher performance than single-learning methods. We further implement the model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and prove that the model can fully utilize neuromorphic many-core architecture to develop hybrid computation paradigm.

cMOOCs and Global Learning: An Authentic Alternative

2013 ◽  
Vol 17 (2) ◽  
Author(s):  
Carol Yeager ◽  
Betty Hurley-Dasgupta ◽  
Catherine A. Bliss
Keyword(s):  
Online Courses ◽  
Lessons Learned ◽  
Massive Open Online Courses ◽  
Research Review ◽  
Press Coverage ◽  
Open Educational Resource ◽  
Educational Resource ◽  
Global Learning ◽  
Massive Open Online ◽  
Definition Of

Massive Open Online Courses (MOOCs) continue to attract press coverage as they change almost daily in their format, number of registrations and potential for credentialing. An enticing aspect of the MOOC is its global reach. In this paper, we will focus on a type of MOOC called a cMOOC, because it is based on the theory of connectivism and fits the definition of an Open Educational Resource (OER) identified for this special edition of JALN. We begin with a definition of the cMOOC and a discussion of the connectivism on which it is based. Definitions and a research review are followed with a description of two MOOCs offered by two of the authors. Research on one of these MOOCs completed by a third author is presented as well. Student comments that demonstrate the intercultural connections are shared. We end with reflections, lessons learned and recommendations.

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