The design of TSK-type fuzzy controllers using a new hybrid learning approach

2006 ◽  
Vol 20 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Cheng-Jian Lin ◽  
Yong-Ji Xu
Keyword(s):  
Hybrid Learning ◽  
Learning Approach ◽  
Fuzzy Controllers

scholarly journals A Trusted Hybrid Learning Approach to Secure Edge Computing

2021 ◽  
pp. 1-1
Author(s):  
Hichem Sedjelmaci ◽  
Sidi Mohammed Senouci ◽  
Nirwan Ansari ◽  
Abdelwahab Boualouache
Keyword(s):  
Hybrid Learning ◽  
Edge Computing ◽  
Learning Approach

scholarly journals A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 739 ◽  
Author(s):  
Seung-Jun Shin ◽  
Young-Min Kim ◽  
Prita Meilanitasari
Keyword(s):  
Machine Tool ◽  
Process Planning ◽  
Predictive Modeling ◽  
Manufacturing Systems ◽  
Minimum Energy ◽  
Hybrid Learning ◽  
Prototype System ◽  
Learning Approach ◽  
Machining Processes ◽  
Self Learning

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

Deployment of a Web Based Critiquing System for Essay Writing in Hybrid Learning Environment

2010 ◽  
pp. 393-405
Author(s):  
Fion S.L. Lee ◽  
Kelvin C.K. Wong ◽  
William K.W. Cheung ◽  
Cynthia F.K. Lee
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Hybrid Learning ◽  
Writing Pedagogy ◽  
Learning Approach ◽  
School Students ◽  
Web Based ◽  
Essay Writing ◽  
Feasibility Evaluation ◽  
Hybrid Learning Environment

This chapter describes the use of a Web-based essay critiquing system and its integration into in a series of composition workshops for a group of secondary school students in Hong Kong. It begins with a review and application of the hybrid learning approach, followed by a description of latent semantic analysis, a methodology for corpus preparation. Then, the distribution computing architecture for essay critiquing system is described. It explicates the way in which the system is integrated with a writing pedagogy implemented in the workshop and the feasibility evaluation result is derived. The positive result confirms the benefits of hybrid learning.

scholarly journals Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xin-Yu Tu ◽  
Bo Zhang ◽  
Yu-Peng Jin ◽  
Guo-Jian Zou ◽  
Jian-Guo Pan ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Hybrid Learning ◽  
Time Span ◽  
Learning Approach ◽  
Time Decay ◽  
Changing Trends ◽  
Decay Factor ◽  
Changing Trend ◽  
The Impact

Air pollution has become a critical issue in human’s life. Predicting the changing trends of air pollutants would be of great help for public health and natural environments. Current methods focus on the prediction accuracy and retain the forecasting time span within 12 hours. Shorter time span decreases the practicability of these perditions, even with higher accuracy. This study proposes an attention and autoencoder (A&A) hybrid learning approach to obtain a longer period of air pollution changing trends while holding the same high accuracy. Since pollutant concentration forecast highly relates to time changing, quite different from normal prediction problems like autotranslation, we integrate “time decay factor” into the traditional attention mechanism. The time decay factor can alleviate the impact of the value observed from a longer time before while increasing the impact of the value from a closer time point. We also utilize the hidden states in the decoder to build connection between history values and current ones. Thus, the proposed model can extract the changing trend of a longer history time span while coping with abrupt changes within a shorter time span. A set of experiments demonstrate that the A&A learning approach can obtain the changing trend of air pollutants, like PM2.5, during a longer time span of 12, 24, or even 48 hours. The approach is also tested under different pollutant concentrations and different periods and the results validate its robustness and generality.

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