Computational intelligence, fuzzy systems, and machine learning: Academic vs industrial learning

2015 ◽  
Author(s):  
Roman Neruda ◽  
Juan Carlos Figueroa-Garcia
Keyword(s):  
Machine Learning ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Industrial Learning

Novel approaches in machine learning and computational intelligence

2013 ◽  
Vol 112 ◽  
pp. 1-3
Author(s):  
Alessio Micheli ◽  
Frank-Michael Schleif ◽  
Peter Tiňo
Keyword(s):  
Machine Learning ◽  
Computational Intelligence ◽  
Novel Approaches

Applications of Data-Driven Modelling and Machine Learning in Control of Water Resources

2011 ◽  
pp. 197-217 ◽  
Author(s):  
D. P. Solomatine
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Computational Intelligence ◽  
Flood Control ◽  
Water Levels ◽  
Data Driven ◽  
Real Time Control ◽  
Modeling And Control ◽  
Physically Based ◽  
And Control

Traditionally, management and control of water resources is based on behavior-driven or physically based models based on equations describing the behavior of water bodies. Since recently models built on the basis of large amounts of collected data are gaining popularity. This modeling approach we will call data-driven modeling; it borrows methods from various areas related to computational intelligence—machine learning, data mining, soft computing, etc. The chapter gives an overview of successful applications of several data-driven techniques in the problems of water resources management and control. The list of such applications includes: using decision trees in classifying flood conditions and water levels in the coastal zone depending on the hydrometeorological data, using artificial neural networks (ANN) and fuzzy rule-based systems for building controllers for real-time control of water resources, using ANNs and M5 model trees in flood control, using chaos theory in predicting water levels for ship guidance, etc. Conclusions are drawn on the applicability of the mentioned methods and the future role of computational intelligence in modeling and control of water resources.

Cognitive Intelligence

2016 ◽  
Vol 10 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Yingxu Wang ◽  
Bernard Widrow ◽  
Lotfi A. Zadeh ◽  
Newton Howard ◽  
Sally Wood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computational Intelligence ◽  
Cognitive Systems ◽  
Cognitive Computing ◽  
Cognitive Intelligence ◽  
Basic Studies ◽  
Deep Reasoning ◽  
Intelligence Theories ◽  
The Brain

The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems

2018 ◽  
Vol 70 ◽  
pp. 131-146 ◽  
Author(s):  
J. Mathew ◽  
J. Griffin ◽  
M. Alamaniotis ◽  
S. Kanarachos ◽  
M.E. Fitzpatrick
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Residual Stresses ◽  
Fuzzy Systems ◽  
Neuro Fuzzy

scholarly journals Evolutionary Machine Learning for Optimal Polar-Space Fuzzy Control of Cyber-Physical Mecanum Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1945
Author(s):  
Hsu-Chih Huang ◽  
Jing-Jun Xu
Keyword(s):  
Machine Learning ◽  
Fuzzy Control ◽  
Physical System ◽  
Fuzzy Systems ◽  
Online Control ◽  
Polar Space ◽  
Robotic System ◽  
Lyapunov Theory ◽  
Flower Pollination Algorithm ◽  
Cyber Physical System

This paper contributes to the development of evolutionary machine learning (EML) for optimal polar-space fuzzy control of cyber-physical Mecanum vehicles using the flower pollination algorithm (FPA). The metaheuristic FPA is utilized to design optimal fuzzy systems, called FPA-fuzzy. In this hybrid computation, both the fuzzy structure and the number of IF–THEN rules are optimized through the FPA evolutionary process. This approach overcomes the drawback of the structure tuning problem in conventional fuzzy systems. After deriving the polar-space kinematics model of Mecanum vehicles, an optimal EML FPA-fuzzy online control scheme is synthesized, and the global stability is proven via Lyapunov theory. An embedded cyber-physical robotic system is then constructed using the typical 5C strategy. The proposed FPA-fuzzy computation collaborates with the advanced sensors and actuators of Mecanum vehicles to design a cyber-physical robotic system. Compared with conventional Cartesian-space control methods, the proposed EML FPA-fuzzy has the advantages of metaheuristics, fuzzy online control, and cyber-physical system design in polar coordinates. Finally, the mechatronic design and experimental setup of a Mecanum vehicle cyber-physical system is constructed. Through experimental results and comparative works, the effectiveness and merit of the proposed methods are validated. The proposed EML FPA-fuzzy control approach has theoretical and practice significance in terms of its real-time capability, online parameter tuning, convergent behavior, and hybrid artificial intelligence.

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