Nov 26, 2013 · This F-CONFIS is a new type of a neural network whose links are with dependent and repeated weights between the input layer and hidden layer.
In this paper, a fuzzy neural network (FNN) is transformed into an equivalent three-layer fully connected neural inference system (F-CONFIS). This F-CONFIS ...
The bounded capacity of fuzzy neural networks (FNNs) via a new ...
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The bounded capacity of fuzzy neural networks (FNNs) via a new fully connected neural fuzzy inference system (F-CONFIS) with its applications. Jing Wang, Chi ...
The capacity of Fuzzy Neural Network is explored and has its emerging values in all engineering applications using FNN, such as intelligent adaptive control ...
Jun 22, 2023 · The bounded capacity of fuzzy neural networks (FNNs) via a new fully connected neural fuzzy inference system (F-CONFIS) with its applications.
Abstract—In this paper, a fuzzy neural network (FNN) is trans- formed into an equivalent three-layer fully connected neural infer- ence system (F-CONFIS).
A new learning algorithm for a fully connected neuro-fuzzy ... - PubMed
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The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind ...
Missing: Bounded Capacity (FNNs) Applications.
Apr 1, 2024 · The bounded capacity of fuzzy neural networks (fnns) via a new fully connected neural fuzzy inference system (f-confis) with its applications ...
A new training algorithm with dropout technique for FNN is proposed via its equivalent F-CONFIS, which has its rising values in all practical applications, ...
The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier.