Granular approach for evolving system modeling

Authors:
Daniel Leite;Pyramo Costa, Jr.;Fernando Gomide
Affiliations:
University of Campinas, School of Electrical and Computer Engineering, Brazil;Pontifical Catholic University of Minas Gerais, Graduate Program in Electrical Engineering, Brazil;University of Campinas, School of Electrical and Computer Engineering, Brazil
Venue:
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Year:
2010

Citing 11
Cited 1

Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic

Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Interval arithmetic: From principles to implementation

Journal of the ACM (JACM)
Machine Learning

Machine Learning
Evolving Connectionist Systems: The Knowledge Engineering Approach

Evolving Connectionist Systems: The Knowledge Engineering Approach
A Ten-year Review of Granular Computing

GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing

Fuzzy Systems Engineering: Toward Human-Centric Computing
Evolving granular classification neural networks

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An approach to online identification of Takagi-Sugeno fuzzy models

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Fuzzy-Rule-Based Classifiers From Data Streams

IEEE Transactions on Fuzzy Systems
Guest Editorial Evolving Fuzzy Systems–-Preface to the Special Section

IEEE Transactions on Fuzzy Systems
General fuzzy min-max neural network for clustering and classification

IEEE Transactions on Neural Networks

Thermal modeling of power transformers using evolving fuzzy systems

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.