A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade
This work addresses a domain-specific challenge in signal processing for applications requiring real-time handling of complex signals, but it appears incremental as it builds on existing cascade systems.
The paper tackles the problem of processing multidimensional time series in online mode for non-stationary stochastic and chaotic signals, proposing a new cascade neuro-fuzzy system with neuron pool optimization that achieves computational simplicity and combines tracking and filtering capabilities.
A new architecture and learning algorithms for the multidimensional hybrid cascade neural network with neuron pool optimization in each cascade are proposed in this paper. The proposed system differs from the well-known cascade systems in its capability to process multidimensional time series in an online mode, which makes it possible to process non-stationary stochastic and chaotic signals with the required accuracy. Compared to conventional analogs, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.