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Fuzzy neural network section

Evaluation of fuzzy regression models by fuzzy neural network

Evaluation of fuzzy regression models by fuzzy neural network

... and fuzzy nonlinear regression model Y i = A 0 e A 1 x i1 + A 2 x i2 + A n x in , where i indexes the different observations, x i1 , x i2 , ...the fuzzy neural network, which is also a ...

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HANDWRITTEN CHARACTER RECOGNITION USING FUZZY NEURAL NETWORK

HANDWRITTEN CHARACTER RECOGNITION USING FUZZY NEURAL NETWORK

... drive; Section 3 gives the structure of the proposed control ...and fuzzy PD control design are discussed in sections 4,5 and 6. Section 7 and 8 provide the simulation results and ...known, ...

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Design of Hybrid Fuzzy Neural Network for Function Approximation

Design of Hybrid Fuzzy Neural Network for Function Approximation

... 2. Fuzzy sets for fuzzy inputs Numeric nodes accept numeric inputs and fuzzify them into Gaussian fuzzy ...weight fuzzy sets since this facili- tates subsethood calculations detailed in ...

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Evolving Fuzzy Neural Network for Phishing s Detection

Evolving Fuzzy Neural Network for Phishing s Detection

... framework section: Our proposed framework explained clearly in ...Evolving Fuzzy Neural Networks (Kasabov and Woodford, 1999) to build PENF, for detection and prediction phishing emails in online ...

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Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network

Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network

... the fuzzy regression model and its application to the analyses and forecasting from classical regression model of ...In Section 2, we briefly describe some basic notions of linear ...in Section 3, we ...

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Utilizing a new feed-back fuzzy neural network for solving a system of
 fuzzy equations

Utilizing a new feed-back fuzzy neural network for solving a system of fuzzy equations

... feed-back neural net- works to obtain an approximate real solution of fuzzy equations system (if ...purposed fuzzy feed-back neural net- work, the connection weights and the fuzzy out- ...

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Receptive field optimisation and supervision of a fuzzy spiking neural network

Receptive field optimisation and supervision of a fuzzy spiking neural network

... this section, unsupervised and supervised learning methods, dynamic synapses and RFs are ...of neural networks involves the changing of synaptic weights in the network in order for the network ...

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Evolving Fuzzy Min-max Neural Network for Outlier Detection

Evolving Fuzzy Min-max Neural Network for Outlier Detection

... in section-6 and references are cited at the ...a fuzzy concept and required in many real time operations, we believe that the hybridization of fuzzy logic and neural network for ...

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A Fuzzy Probabilistic Neural Network for Student’s Academic Performance Prediction

A Fuzzy Probabilistic Neural Network for Student’s Academic Performance Prediction

... Probabilistic Neural Network hybridized with fuzzy logic to predict students’ performance and to classify them according to their similar ...Probabilistic Neural Network (PNN) has been ...

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COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK

COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK

... In this paper several evolutionary algorithms are used for adjusting the parameters of FWNN on some test functions and the results are compared. The paper is organized as follows: Section 2 introduces FWNN and the ...

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A Novel Fuzzy and Artificial Neural Network Representation of Overcurrent Relay Characteristics

A Novel Fuzzy and Artificial Neural Network Representation of Overcurrent Relay Characteristics

... It means that for each value of current multiplier setting, there is a specific equation. But for the mathematical nonlinear model of OC relay, only one equation must be selected. It is not straightforward because in the ...

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Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems

Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems

... The whole system was decoupled into two subsystems such that each subsystem had a separate control target expressed in terms of a sliding surface. Then, information from the secondary target conditions the main target, ...

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Fuzzy Signature Neural Network

Fuzzy Signature Neural Network

... l Data-driven way to create fuzzy signatures l Self-determined fuzzy signatures number l Improve HE’s fuzzy signature neural. network[r] ...

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Neural Network Regressions with Fuzzy Clustering

Neural Network Regressions with Fuzzy Clustering

... Neural Network Regressions with Fuzzy Clustering ...hybrid neural network regression models with unsupervised fuzzy clustering is proposed for clustering nonparametric regression ...

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Fuzzy Neural Network for Clustering and Classification

Fuzzy Neural Network for Clustering and Classification

... a fuzzy neural network for clustering and ...this fuzzy neural network two training algorithm are implemented for clustering and ...General Fuzzy min max Neural ...

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Review Computer Network Routing with a Fuzzy Neural Network

Review Computer Network Routing with a Fuzzy Neural Network

... applied fuzzy reasoning enhanced by a neural ...computer network greatly affects the travel time for all types of ...computer network can delay or completely stop the transmission of ...

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Simulation of a neural network-driven fuzzy controller

Simulation of a neural network-driven fuzzy controller

... This Thesis is brought to you for free and open access by the Thesis/Dissertation Collections at RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For ...

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A Fuzzy Neural Network Fault Diagnostic System

A Fuzzy Neural Network Fault Diagnostic System

... the fuzzy- neural network ...the neural network and the fuzzy logic control for complex and critical ...uncertain fuzzy logic information, and the self-learning and ...

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Fuzzy and Neural Network Models for Analyses of Piles

Fuzzy and Neural Network Models for Analyses of Piles

... developed neural network model, pile capacity predictions were made using back-propagation neural network as well as three commonly used empirical methods, and they were also compared with ...

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Numerical solution of hybrid fuzzy differential equations by fuzzy neural network

Numerical solution of hybrid fuzzy differential equations by fuzzy neural network

... of Fuzzy Differential Equations years. The concept of fuzzy derivative was first introduced by Chang and Zadeh [11], it was fol- lowed up by Dubois and Prade [13] who used the extension principle in their ...

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