An Overview of Different Neural Network Architectures

Skansi, Sandro

doi:10.1007/978-3-319-73004-2_10

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Currently, a data-driven artificial neural network (ANN) combined with a sound-absorbing coating composed of cavities in a viscoelastic material has advantages that traditional methods lack. For example, no feature engineering is required, which effectively overcomes the shortcomings of the difficulty of meshing in the finite element method (FEM); data-driven methods apply end-to-end learning without intermediate processes; and large amounts of information can be generated in batches, which saves time and economic costs [24][25][26]. ANNs have achieved world-renowned success in photoacoustic tomography, material defect detection, robot vision, natural language processing, energy prediction and other fields, and they are currently a popular research topic [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

Sun¹,

Chen²,

Bai³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest. Traditional forward research methods are unable to manipulate sound waves accurately and effectively, are difficult to analyse, have time-consuming solution processes, and have large optimization search spaces. To address these issues, this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance. First, an autoencoder (AE) model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating, the material parameters are extracted into the latent feature space by the encoder, and the decoder model is saved. The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron (MLP). Then, the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters. The accuracy of the inversion result is 95.08%. Finally, a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters. The speed of a single test target is 80 times faster than that of the finite element method (FEM). Furthermore, sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted, and their actual sound absorption performance is predicted by the proposed method. The proposed deep learning-based inversion research method provides a solution for low-frequency, wide-band, strong attenuation, and precisely controlled sound waves. It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance. The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.

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Section: Introductionmentioning

confidence: 99%

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

Sun¹,

Chen²,

Bai³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…This study focused on Hopfield Neural Network (HNN) which is an important type of Artificial neural network (ANN) that simulates human network associative memory invented by John J. Hopfield in 1982 (Skansi, 2018). The structure of HNN consists of a single layer with one or more recurrent or fully interconnected neural networks.…”

Section: Introductionmentioning

confidence: 99%

An optimal representation of Random Maximum kSatisfiability on a Hopfield Neural Network for High order logic(k 3)

Abubakar

2021

KJS publishes peer-review articles in Mathematics, Computer Sci

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This paper proposed a new logical rule by incorporating Random maximum kSatsifiability (RANMAXkSAT) in the Hopfield neural network (HNN) as a single network model (HNN-RANMAXkSAT). The purpose is to combine the optimization capacity of the Hopfield neural network (HNN) for optimal representation to random maximum kSatsifiability (MAXRANkSAT). The energy function of a Hopfield neural network has been considered as a programming language for dynamics minimization mechanism. Several optimization and search problems associated with machine learning (ML), decision Science (DS) and artificial intelligence (AI) have been expressed on the Hopfield neural network(HNN) optimally by modelling the problem into variables to minimize the objective function that corresponds to Lyapunov energy function. The computer simulation has been developed based on RANMAXkSAT to explore the feasibility of a Hopfield neural network as a neuro-symbolic integration model in carrying out RANMAXkSAT logic programming optimally. The proposed model has been compared with the existing models published in the literature in term of Global minimum ratio (zM), Fitness energy landscapes (FEL), Root Means square error (RMSE), Mean absolute errors and computation time (CPU). Hence, based on the experimental simulation results, it revealed that the RANMAXkSAT can optimally and effectively represented in the Hopfield neural network (HNN) with 85.1 % classification accuracy.

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An Overview of Different Neural Network Architectures

Cited by 2 publications

References 6 publications

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

An optimal representation of Random Maximum kSatisfiability on a Hopfield Neural Network for High order logic(k 3)

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