A course in fuzzy systems and control
A course in fuzzy systems and control
Hierarchical genetic fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
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)
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Applied Soft Computing
Fuzzy-genetic algorithm for automatic fault detection in HVAC systems
Applied Soft Computing
Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
Applied Soft Computing
A neuro-coevolutionary genetic fuzzy system to design soft sensors
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Engineering Applications of Artificial Intelligence
Genetic algorithm based NARX model identification for evaluation of insulin sensitivity
Applied Soft Computing
Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy identification and predictive control for industrial processes
Expert Systems with Applications: An International Journal
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This paper proposes a new method for soft sensors (SS) design for industrial applications based on a Takagi-Sugeno (T-S) fuzzy model. The learning of the T-S model is performed from input/output data to approximate unknown nonlinear processes by a coevolationary genetic algorithm (GA). The proposed method is an automatic tool for SS design since it does not require any prior knowledge concerning the structure (e.g. the number of rules) and the database (e.g. antecedent fuzzy sets) of the T-S fuzzy model, and concerning the selection of the adequate input variables and their respective time delays for the prediction setting. The GA approach is composed by five hierarchical levels and has the global goal of maximizing the prediction accuracy. The first level consists in the selection of the set of input variables and respective delays for the T-S fuzzy model. The second level considers the encoding of the membership functions. The individual rules are defined at the third level, the population of the set of rules is treated in fourth level, and a population of fuzzy systems is handled at the fifth level. To validate and demonstrate the performance and effectiveness of the proposed algorithm, it is applied on two prediction problems. The first is the Box-Jenkins benchmark problem, and the second is the estimation of the flour concentration in the effluent of a real-world wastewater treatment system. Simulation results are presented showing that the developed evolving T-S fuzzy model can identify the nonlinear systems satisfactorily with appropriate input variables and delay selection and a reasonable number of rules. The proposed methodology is able to design all the parts of the T-S fuzzy prediction model. Moreover, presented comparison results indicate that the proposed method outperforms other previously proposed methods for the design of prediction models, including methods previously proposed for the design of T-S models.