Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation

2011 ◽  
Vol 5 (4) ◽  
pp. 409 ◽  
Author(s):  
Chunshien Li ◽  
Tai Wei Chiang
Keyword(s):  
Function Approximation ◽  
Fuzzy Model ◽  
Hybrid Learning ◽  
Learning Approach

Generating Singleton Fuzzy Models from Data with Interpretable Partition

2012 ◽  
Vol 629 ◽  
pp. 784-791 ◽  
Author(s):  
Juan Contreras
Keyword(s):  
Function Approximation ◽  
Least Squares Method ◽  
Fuzzy Model ◽  
Approximation Problem ◽  
Classification Problem ◽  
Descent Method ◽  
Recursive Least Squares ◽  
Gradient Descent Method ◽  
Recursive Least Squares Method ◽  
Triangular Membership Function

This paper presents a new methodology for obtaining singleton fuzzy model from experimental data. Each input variable is partitioned into triangular membership functions so that consecutive fuzzy sets exhibit and specific overlapping of 0.5. The recursive least squares method is employed to adjust the singleton consequences and the gradient descent method is employed to update only the modal value of each triangular membership function to preserve the overlap and reducing the number of parameters to be estimated. Applications to a function approximation problem and to a pattern classification problem are illustrated.

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.

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