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This paper introduces a genetic fuzzy system to control short and long term memory of tabu search algorithms. The genetic fuzzy system involves learning of the knowledge base and a rule selection procedure. The aim is to trade-off exploration and exploitation behavior of the search, and to handle high dimensional optimization problems. The genetic fuzzy system approach introduces a high level of autonomy...
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 and uses nullnorm based neurons to classify...
This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models...
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