Data Driven Level Set Fuzzy and Neural Modeling

Abstract Data driven modeling has been a major approach for learning and understanding systems, ranging from medical, biological, environmental, meteorological, transportation, and economic systems to complex dynamic information, engineering, and hybrid systems. Computational intelligence, rooted in fuzzy systems, neural networks, evolutionary computation and their hybridizations, is a key driving force in the current data driven system modeling effort. This paper addresses data driven fuzzy modeling and neural modeling structures, and evaluates their performances in nonlinear dynamic systems modeling tasks. Data driven fuzzy modeling and neural modeling structures are powerful modeling paradigms that compete closely, often surpassing most of the alternative state of the art methods such as gradient boosting, kernel ridge regression, and Gaussian processes. In particular, the complexity and approximation capabilities of the data driven fuzzy modeling, long-short term memory, convolutional, and hybrid neural modeling structures are evaluated, and their usefulness are discussed in front of the accuracy of the predictions, and the complexity of the models they produce.