Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks

This paper proposes an architecture and learning algorithms for a cascade neo-fuzzy system based on pools of extended neo-fuzzy neurons. The proposed system is different from existing cascade systems in its capability to operate in an online mode, which allows it to work with non-stationary and stochastic non-linear chaotic signals that come in the form of data streams. A new pool optimization procedure is introduced. Compared to conventional analogues, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.

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