Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

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ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

Frontiers in Neuroinformatics ◽

10.3389/fninf.2019.00070 ◽

2019 ◽

Vol 13 ◽

Cited By ~ 14

Author(s):

Taban Eslami ◽

Vahid Mirjalili ◽

Alvis Fong ◽

Angela R. Laird ◽

Fahad Saeed

Keyword(s):

Autism Spectrum Disorder ◽

Hybrid Learning ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Fmri Data ◽

Learning Approach

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

Processes ◽

10.3390/pr7100739 ◽

2019 ◽

Vol 7 (10) ◽

pp. 739 ◽

Cited By ~ 2

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.

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

Handbook of Research on Hybrid Learning Models ◽

10.4018/978-1-60566-380-7.ch024 ◽

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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