Evolutionary-Fuzzy Control

Siddique, Nazmul

doi:10.1007/978-3-319-02135-5_6

Cited by 2 publications

(2 citation statements)

References 74 publications

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“…A mathematical model based on the physiological and functional characteristics of the neurological system and brain of a human is the artificial neural network (ANN) algorithm under discussion . A selection of the ANN’s features are its fast speed for parallel computing, nonlinear calculations, generalization, adaptability, error tolerance, sensitivity to noisy data, and training . The ANN algorithm employs data obtained from DFT simulation to learn the network design, and the network matrix is produced by optimizing each network variable.…”

Section: Machine Learning Theorymentioning

confidence: 99%

The Role of Transition Metals on CeO₂ Supported for CO₂ Adsorption by DFT and Machine Learning Analysis

Mashhadimoslem,

Karimi,

Abdol

et al. 2024

Ind. Eng. Chem. Res.

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Section: Machine Learning Theorymentioning

confidence: 99%

The Role of Transition Metals on CeO₂ Supported for CO₂ Adsorption by DFT and Machine Learning Analysis

Mashhadimoslem,

Karimi,

Abdol

et al. 2024

Ind. Eng. Chem. Res.

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“…A fully connected neural network (FCNN) is one of the most basic artificial neural network structures, also known as a multilayer perceptron (MLP). FCNN consists of an input layer, a hidden layer, and an output layer 8 . In FCNN, each neuron is connected to all the neurons in the neighboring layers, forming a densely connected structure.…”

Section: Fully Connected Neural Networkmentioning

confidence: 99%

Fully connected-based nonnegative matrix factorization neural network

Liu,

Wei,

Yang

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Nonnegative matrix factorization (NMF) is a powerful method for feature extraction, offering explanatory and dimensionality reduction. Alternatively, combining NMF with a neural network requires iterative optimization of an objective function, followed by constructing a specialized neural network based on the derived formula. The interpretability and universality of this approach are limited. To address these issues, this paper introduces a novel model called FCNMFN, which leverages a fully connected neural network to implement NMF. In this model, each layer of the fully connected neural network corresponds to the transpose of the base matrix, the coefficient matrix, and the sample matrix of NMF. This design ensures strong interpretability while achieving nonnegative matrix factorization. To demonstrate the effectiveness of the proposed model, we apply it to emotion recognition using the DEAP dataset. Experimental results confirm its efficacy and showcase its potential in accurately identifying and analyzing emotions.

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Evolutionary-Fuzzy Control

Cited by 2 publications

References 74 publications

The Role of Transition Metals on CeO₂ Supported for CO₂ Adsorption by DFT and Machine Learning Analysis

The Role of Transition Metals on CeO₂ Supported for CO₂ Adsorption by DFT and Machine Learning Analysis

Fully connected-based nonnegative matrix factorization neural network

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Evolutionary-Fuzzy Control

Cited by 2 publications

References 74 publications

The Role of Transition Metals on CeO2 Supported for CO2 Adsorption by DFT and Machine Learning Analysis

The Role of Transition Metals on CeO2 Supported for CO2 Adsorption by DFT and Machine Learning Analysis

Fully connected-based nonnegative matrix factorization neural network

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The Role of Transition Metals on CeO₂ Supported for CO₂ Adsorption by DFT and Machine Learning Analysis

The Role of Transition Metals on CeO₂ Supported for CO₂ Adsorption by DFT and Machine Learning Analysis