A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade

TL;DR

This paper introduces a novel multidimensional cascade neuro-fuzzy system with neuron pool optimization, enabling online processing of complex time series, including non-stationary and chaotic signals, with improved accuracy and simplicity.

Contribution

It presents a new architecture and learning algorithms for a multidimensional hybrid cascade neural network with optimized neuron pools at each stage.

Findings

Capable of processing non-stationary stochastic signals

Provides computational simplicity compared to analogs

Achieves high accuracy in filtering and tracking

Abstract

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.