TY - GEN
T1 - Granular approach for evolving system modeling
AU - Leite, Daniel
AU - Costa, Pyramo
AU - Gomide, Fernando
PY - 2010
Y1 - 2010
N2 - In this paper we introduce a class of granular evolving system modeling approach within the framework of interval analysis. Our aim is to present an interval-based learning algorithm which develops both, granular and singular approximations of nonlinear nonstationary functions using singular data. The algorithm is capable of incrementally creating/adapting both model parameters and structure. These are key features in nonlinear systems modeling. In addition, interval analysis provides rigorous bounds on approximation errors, rounding errors, and on uncertainties in data propagated during computations. The learning algorithm is simple and particularly suited to process stream of data in real time. In this paper we focus on the foundations of the approach and on the details of the learning algorithm. An application concerning economic time series forecasting illustrates the usefulness and efficiency of the approach.
AB - In this paper we introduce a class of granular evolving system modeling approach within the framework of interval analysis. Our aim is to present an interval-based learning algorithm which develops both, granular and singular approximations of nonlinear nonstationary functions using singular data. The algorithm is capable of incrementally creating/adapting both model parameters and structure. These are key features in nonlinear systems modeling. In addition, interval analysis provides rigorous bounds on approximation errors, rounding errors, and on uncertainties in data propagated during computations. The learning algorithm is simple and particularly suited to process stream of data in real time. In this paper we focus on the foundations of the approach and on the details of the learning algorithm. An application concerning economic time series forecasting illustrates the usefulness and efficiency of the approach.
UR - http://www.scopus.com/inward/record.url?scp=77954882995&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-14049-5_35
DO - 10.1007/978-3-642-14049-5_35
M3 - Conference contribution
AN - SCOPUS:77954882995
SN - 3642140483
SN - 9783642140488
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 340
EP - 349
BT - Computational Intelligence for Knowledge-Based Systems Design - 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Proceedings
T2 - 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010
Y2 - 28 June 2010 through 2 July 2010
ER -