A class of Monotone Fuzzy rule-based Wiener systems with an application to Li-ion battery modelling. (September 2017)

• Record Type: Journal Article
• Authors: Sánchez, Luciano; Couso, Inés; Blanco, Cecilio
• Is Part Of: Engineering applications of artificial intelligence. Volume 64(2017:Apr.)
• Page Start: 367
• Page End: 377
• Publication Date: 2017-09
• Subjects: Fuzzy rule-based models -- Dynamic systems -- Monotonic decision systems -- Li-ion batteries; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.06.029
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: A class of Fuzzy rule-based Monotone Wiener Models (FMWMs) is introduced. These are transformation models comprising a linear dynamical block and a memoryless nonlinearity. The smoothest dynamical block that has an output which is comonotonic with the training data is sought. The dependence between the output of the linear block and the output of the system is described via a set of fuzzy rules. This paper considers systems with a sensitive dependence on the initial conditions and also with a moderate amount of uncertainty in the initial state. A new learning algorithm is proposed that makes use of recent statistical tests for assessing the comonotonicity of imprecisely perceived sequences of data. The main aim of the proposed models is to estimate different health parameters of rechargeable batteries for automotive use. For this practical application, FMWMs are shown to improve a selection of models with a varying degree of embedded domain knowledge, ranging from first-principles models to universal approximators.