Fuzzy modeling and control of multilayer incinerator
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
System identification: theory for the user
System identification: theory for the user
Fast and accurate timing simulation with regionwise quadratic models of MOS I-V characteristics
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Advances in Fuzzy Control
Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study
Neural Processing Letters
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
IEEE Computational Intelligence Magazine
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Circuits and Systems for Video Technology
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Time series forecasting in highly nonlinear and chaotic systems is a challenging research area with a variety of applications in economics, environmental sciences and various fields of engineering. This paper presents a novel Locally Quadratic Fuzzy Neural Model (LQFNM) to forecast the behavior of highly nonlinear and chaotic time series. It is based on the idea of approximating a nonlinear function with interpolated local quadratic models using a tree construction algorithm. A fast heuristic learning algorithm is integrated in the model to derive the structure as well as the parameters of the Locally Quadratic Models. Four different case studies are conducted in which the performance of the method is evaluated through comparisons with other techniques available in literature. The results confirm the accuracy and reliability of the presented method. The proposed LQFNM can be applied to time series forecasting in a wide range of real world applications.