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Neuro-Fuzzy Kolmogorov's Network for Time Series Prediction and Pattern Classification
TLDR
The NFKN is the development of the previously proposed neural and fuzzy systems using the famous Kolmogorov's superposition theorem (KST) and is linear in both the hidden and output layer parameters, so it can be trained with very fast and simple procedures.
An adaptive learning algorithm for a neo fuzzy neuron
TLDR
The proposed algorithm has both the tracking and filtering properties, so the NFN can be effectively used for prediction, filtering, and restoration of non-stationary noisy stochastic and chaotic signals.
Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks
Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks
TLDR
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.
Universal Approximator Employing Neo-Fuzzy Neurons
A novel fuzzy neural network, called Fuzzy Kolmogorov’s Network (FKN), is considered. The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer
ADAPTIVE HUMAN MACHINE INTERACTION APPROACH FOR FEATURE SELECTION-EXTRACTION TASK IN MEDICAL DATA MINING
TLDR
New approach to evaluation of medical features information quantity, based on optimized combination of feature selection and feature extraction methods is proposed, which allows physicians to have the most informative features without losing their linguistic interpreting.
THE CASCADE NEO-FUZZY ARCHITECTURE AND ITS ONLINE LEARNING ALGORITHM
TLDR
A learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks.
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