Daniel Leite

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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)
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)
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)
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract—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(More)
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)
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 non-stationary granular data streams. This work considers(More)