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Fuzzy computing theory, hardware, and applications
Fuzzy computing theory, hardware, and applications
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Fuzzy computing theory, hardware, and applications

Madan M Gupta; Takeshi Yamakawa 1946-

Amsterdam North-Holland New York New York, N.Y., U.S.A. Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co. 1988

Available at IME - Inst. Matemática e Estatística    (QA860.20.C3 G977f ) and other locations(GetIt)

2
A first course in fuzzy and neural control
A first course in fuzzy and neural control
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A first course in fuzzy and neural control

Hung T Nguyen 1944-

Boca Raton, FL Chapman & Hall CRC c2003

Available at EPELM - Esc. Politécnica-Bib Eng Elet., Mec. e Naval    (004.8 F519 )(GetIt)

3
Nonlinear H2/H [infinity] constrained feedback control a practical design approach using neural networks
Nonlinear H2/H [infinity] constrained feedback control a practical design approach using neural networks
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Nonlinear H2/H [infinity] constrained feedback control a practical design approach using neural networks

Murad Abu-Khalaf Jie Huang 1955-; Frank L Lewis

London Springer-Verlag c2006

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4
Models for the modern power grid
Models for the modern power grid
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Models for the modern power grid

Cardieri, Paulo, 1964- Universidade Estadual de Campinas (Unicamp); Universidade Estadual De Campinas

https://repositorio.unicamp.br/acervo/detalhe/1191204

2014

Online access

5
Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis
Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis
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Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis

Wang, Huanqing ; Chen, Bing ; Liu, Kefu ; Liu, Xiaoping ; Lin, Chong

IEEE transaction on neural networks and learning systems, 2014-05, Vol.25 (5), p.947-958

New York, NY: IEEE

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6
Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks
Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks
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Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks

Chen, C. L. Philip ; Guo-Xing Wen ; Yan-Jun Liu ; Fei-Yue Wang

IEEE transaction on neural networks and learning systems, 2014-06, Vol.25 (6), p.1217-1226

New York, NY: IEEE

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7
Model predictive control: Recent developments and future promise
Model predictive control: Recent developments and future promise
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Model predictive control: Recent developments and future promise

Mayne, David Q.

Automatica (Oxford), 2014-12, Vol.50 (12), p.2967-2986 [Peer Reviewed Journal]

Kidlington: Elsevier Ltd

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Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems
Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems
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Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems

Jiang, Yu ; Jiang, Zhong-Ping

IEEE transaction on neural networks and learning systems, 2014-05, Vol.25 (5), p.882-893

New York, NY: IEEE

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9
Event-Triggering in Distributed Networked Control Systems
Event-Triggering in Distributed Networked Control Systems
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Event-Triggering in Distributed Networked Control Systems

Xiaofeng Wang ; Lemmon, M D

IEEE transactions on automatic control, 2011-03, Vol.56 (3), p.586-601 [Peer Reviewed Journal]

New York, NY: IEEE

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10
A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems
A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems
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A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems

Bechlioulis, Charalampos P. ; Rovithakis, George A.

Automatica (Oxford), 2014-04, Vol.50 (4), p.1217-1226 [Peer Reviewed Journal]

Kidlington: Elsevier Ltd

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