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- Daniel Leite, Pyramo Pires da Costa, Fernando A. C. Gomide
- Neural Networks
- 2013

This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) is able to handle gradual and abrupt parameter changes typical of nonstationary (online) environments. eGNN builds interpretable multi-sized local models using fuzzy neurons for information fusion.… (More)

- Daniel Leite, Rosangela Ballini, Pyramo Pires da Costa, Fernando A. C. Gomide
- Evolving Systems
- 2012

- Daniel Leite, Pyramo Pires da Costa, Fernando A. C. Gomide
- The 2010 International Joint Conference on Neural…
- 2010

In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes… (More)

- Daniel Leite, Reinaldo M. Palhares, Victor C. S. Campos, Fernando A. C. Gomide
- IEEE Trans. Fuzzy Systems
- 2015

Unknown nonstationary processes require modeling and control design to be done in real time using streams of data collected from the process. The purpose is to stabilize the closed-loop system under changes of the operating conditions and process parameters. This paper introduces a model-based evolving granular fuzzy control approach as a step toward the… (More)

- Daniel Leite, Fernando A. C. Gomide, Rosangela Ballini, Pyramo Pires da Costa
- FUZZ-IEEE
- 2011

Modeling large volumes of flowing data from complex systems motivates rethinking several aspects of the machine learning theory. Data stream mining is concerned with extracting structured knowledge from spatio-temporally correlated data. A profusion of systems and algorithms devoted to this end has been constructed under the conceptual framework of granular… (More)

- Daniel F. Leite, Pyramo Costa, Fernando Gomide
- 2009 IEEE Workshop on Evolving and Self…
- 2009

This paper introduces a granular, interval-based evolving modeling (IBeM) approach to develop system models from a stream of data. IBeM is an evolving rule-based modeling scheme that gradually adapts its structure (information granules and rule base) and rules antecedent and consequent parameters from data (inductive learning). Its main purpose is… (More)

- Daniel Leite, Fernando A. C. Gomide
- Combining Experimentation and Theory
- 2012

This work outlines a new approach for online learning from imprecise data, namely, fuzzy set based evolving modeling (FBeM) approach. FBeM is an adaptive modeling framework that uses fuzzy granular objects to enclose uncertainty in the data. The FBeM algorithm is data flow driven and supports learning on an instance-per-instance recursive basis by… (More)

Physical systems change over time and usually produce considerable amount of nonstationary data. Evolving modeling of time varying systems requires adaptive and flexible procedures to deal with heterogeneous data. Granular computing provides a rich framework for modeling time varying systems using nonstationary granular data streams. This work considers… (More)

- Daniel Leite, Pyramo Pires da Costa, Fernando A. C. Gomide
- The 2012 International Joint Conference on Neural…
- 2012

A primary requirement of a broad class of evolving intelligent systems is to process a sequence of numeric data over time. This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) efficiently handles concept changes, distinctive events of nonstationary… (More)