论文信息 - System Modeling Using Type-2 Takagi-Sugeno Fuzzy Systems

System Modeling Using Type-2 Takagi-Sugeno Fuzzy Systems

The development of computational models capable of accurately describe a process’s dynamic response is a task ultimately dependent on its physical phenomena complexity and the type of disturbances that may affect its operation.

[1] David W. Clarke,et al. Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[2] Woei Wan Tan,et al. A simplified type-2 fuzzy logic controller for real-time control. , 2006, ISA transactions.

[3] William L. Luyben,et al. Chemical Reactor Design and Control , 2007 .

[4] Zoltan K. Nagy,et al. Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks , 2007 .

[5] Katsuhiko Ogata,et al. Modern Control Engineering , 1970 .

[6] Jérôme Mendes. Computational Intelligence Methodologies for Control of Industrial Processes , 2014 .

[7] Rolf Isermann,et al. Identification of Dynamic Systems , 2011 .

[8] Karl Johan Åström,et al. Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[9] Niels Kjølstad Poulsen,et al. Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[10] Miroslav Fikar,et al. Transformation of Fuzzy Takagi-Sugeno Models into Piecewise Affine Models , 2007, RSEISP.

[11] Maciej Ławryńczuk,et al. Computationally Efficient Model Predictive Control Algorithms , 2014 .

[12] Maciej Lawrynczuk. Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach , 2014 .

[13] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .