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This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.

This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron. [ABSTRACT FROM AUTHOR]

A task of Data Stream Fuzzy Clustering is considered when data is processed sequentially under a priori uncertainty conditions about both a number of clusters and a degree of clusters’ overlapping. A modified two-layer neuro-fuzzy Kohonen network is used for solving the possibilistic fuzzy clustering tasks. This system tunes centers’ coordinates and membership levels of every pattern to clusters during the self-learning procedure and automatically increases a number of neurons during data processing. A distinguishing feature of the proposed approach is its computational simplicity due to the fact that a recurrent modification of the possibilistic fuzzy clustering procedure is used for tuning the network’s parameters. The proposed neuro-fuzzy system is based on the concepts of evolving systems of Computational Intelligence, the recurrent optimization, the possibilistic fuzzy clustering, and Data Stream Mining. [ABSTRACT FROM AUTHOR]

An evolving cascade neuro-fuzzy system and its online learning procedure are considered in this paper. The system is based on conventional Kohonen neurons. The proposed system solves a clustering task of non-stationary data streams under uncertainty conditions when data come in the form of a sequential stream in an online mode. A quality estimation process is defined by finding an optimal value of the used cluster validity index. [ABSTRACT FROM AUTHOR]