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The bloodsucking hemipteran Rhodnius prolixus is a vector of Chagas' disease, which affects 7-8 million people today in Latin America. In contrast to other hematophagous insects, the triatomine gut is compartmentalized into three segments that perform different functions during blood digestion. Here we report analysis of transcriptomes for each of the(More)
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 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)
The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The suggested eGNN are neural models supported by granule-based learning algorithms whose aim is to tackle classification problems in continuously(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)
—The 2012 FUZZ-IEEE conference competition " Learning Fuzzy Systems from Data " aims to establish the empirical accuracy of fuzzy forecasting algorithms in the domain of prediction of the sales volume of petroleum products. Currently, there are no guidelines or consensus on a best practice methodology. This paper proposes evolving fuzzy linear regression(More)