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Popular among these are the higher-order neurons, fuzzy neurons and other polynomial neurons.
Feature-extraction neuron-fuzzy classification model.
A cerebellar model arithmetic computer (CMAC -based neuron-fuzzy approaCMAC -basedrate system modeling is proposed.
A feature-extraction neuron-fuzzy classification model (FENFCM) has been proposed by Nai Ren Guo et al. [56] that enabled the extraction of feature variables and has provided the classification results.
In this work, a comparative study for image classification and detection is performed based on three soft computing techniques: multilayer perceptron, support vector machine, and adaptive neuron-fuzzy inference system.
The neurons represent fuzzy sets used in the antecedents of fuzzy rules and determine the membership degree of the input.
Fuzzy neurons and fuzzy neural networks (FNN) are constructs of computational intelligence that come with significant learning abilities and inherent transparency (interpretability).
In this study, we introduce a class of neural architectures of self-organizing neural networks (SONN) that is based on a genetically optimized multilayer perceptron with polynomial neurons (PNs) or fuzzy polynomial neurons (FPNs).
These networks consist of a genetically optimized multi-layer with two kinds of heterogeneous neurons such as fuzzy set-based polynomial neurons (FSPNs) and polynomial neurons (PNs).
In this control system, the fuzzy neuron hybrid controller is constructed by the fuzzy PI controller and the neuron controller, the gain of the neuron controller is tuned by using a fuzzy algorithm.
At the antecedents layer, a interval type-2 fuzzy neuron (IT2FN) model is used, and in case of the consequents layer an interval type-1 fuzzy neuron model (IT1FN), in order to fuzzify the rule's antecedents and consequents of an interval type-2 Takagi Sugeno Kang fuzzy inference system (IT2-TSK-FIS).
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