An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm
This work addresses the need for efficient and accurate function approximation in neuro-fuzzy systems, but it appears incremental as it modifies an existing neo-fuzzy neuron.
The authors tackled the problem of improving approximation in neuro-fuzzy systems by proposing an extended neo-fuzzy neuron (ENFN) with enhanced approximating properties and an adaptive learning algorithm that offers tracking and smoothing capabilities, resulting in computational simplicity compared to other neural networks.
A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.