Restricted exponential forgetting in real-time identification
Automatica (Journal of IFAC)
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
A course in fuzzy systems and control
A course in fuzzy systems and control
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
Modeling Uncertainty with Fuzzy Logic: With Recent Theory and Applications
Modeling Uncertainty with Fuzzy Logic: With Recent Theory and Applications
A new T-S fuzzy-modeling approach to identify a boiler-turbine system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Genetic fuzzy system for data-driven soft sensors design
Applied Soft Computing
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This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T-S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T-S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm's performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T-S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T-S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.