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   Articles with "hidden layer" as a keyword



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Dynamic Adjustment of Hidden Layer Structure for Convex Incremental Extreme Learning Machine

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recommendations! Published in 2017 at "Neurocomputing"

DOI: 10.1007/978-3-319-28373-9_30

Abstract: Extreme Learning Machine (ELM) is a learning algorithm based on generalized single-hidden-layer feed-forward neural network. Since ELM has an excellent performance on regression and classification problems, it has been paid more and more attention recently.… read more here.

Keywords: extreme learning; elm; learning machine; hidden layer ... See more keywords

Model NOx emission and thermal efficiency of CFBB based on an ameliorated extreme learning machine

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recommendations! Published in 2018 at "Soft Computing"

DOI: 10.1007/s00500-017-2653-0

Abstract: Extreme learning machine (ELM) is a novel single hidden layer feed-forward network, which has become a research hotspot in various domains. Through in-depth analysis on ELM, there are four factors mainly affect its model performance,… read more here.

Keywords: machine; extreme learning; learning machine; model ... See more keywords

A new variant of restricted Boltzmann machine with horizontal connections

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recommendations! Published in 2018 at "Neural Computing and Applications"

DOI: 10.1007/s00521-018-3460-y

Abstract: Restricted Boltzmann machines (RBMs) are successfully employed to construct deep architectures because their power of expression and the inference is tractable and easy. In this paper, we propose a model named self-connected restricted Boltzmann machine… read more here.

Keywords: horizontal connections; restricted boltzmann; layer; hidden layer ... See more keywords
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N-hidden layer artificial neural network architecture computer code: geophysical application example

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recommendations! Published in 2020 at "Heliyon"

DOI: 10.1016/j.heliyon.2020.e04108

Abstract: We provide a MATLAB computer code for training artificial neural network (ANN) with N+1 layer (N-hidden layer) architecture. Currently, the ANN application to solving geophysical problems have been confined to the 2-layer, i.e. 1-hidden layer,… read more here.

Keywords: code; layer ann; application; hidden layer ... See more keywords

Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks.

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recommendations! Published in 2025 at "Physical review. E"

DOI: 10.1103/9pkk-d4bm

Abstract: Finite-width one hidden layer networks with multiple neurons in the readout layer display nontrivial output-output correlations that vanish in the lazy-training infinite-width limit. In this manuscript we leverage recent progress in the proportional limit of… read more here.

Keywords: output correlations; layer networks; hidden layer; output output ... See more keywords
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Greedy Broad Learning System

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3084610

Abstract: In order to overcome the extremely time-consuming drawback of deep learning (DL), broad learning system (BLS) was proposed as an alternative method. This model is simple, fast, and easy to update. To ensure the fitting… read more here.

Keywords: learning system; bls; hidden layer; layer ... See more keywords
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Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3099492

Abstract: Structural Plasticity (SP) in the brain is a process that allows structural neuronal changes, in response to learning. Spiking Neural Networks (SNN) are an emerging form of artificial neural networks that use brain-inspired techniques to… read more here.

Keywords: incorporating structural; neural networks; hidden layer; spiking neural ... See more keywords

Fuzzy Multiple Hidden Layer Neural Sliding Mode Control of Active Power Filter With Multiple Feedback Loop

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3104030

Abstract: A fuzzy multiple hidden layer neural sliding mode control with multiple feedback loop (FMHLNSMCMFL) is proposed for a single-phase active power filter (APF), where a sliding mode controller is designed to make the current tracking… read more here.

Keywords: sliding mode; control; hidden layer; multiple feedback ... See more keywords

Structured DNN-Based Receiver for Millimeter-Wave MIMO With Nonlinear Distortions

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recommendations! Published in 2022 at "IEEE Wireless Communications Letters"

DOI: 10.1109/lwc.2021.3130894

Abstract: This work deals with the combined effect of nonlinear distortions and inter-channel interference in millimeter wave multi-input multi-output (MIMO) communications. Deep neural networks (DNNs) can be used to handle the effect, but they often require… read more here.

Keywords: millimeter wave; nonlinear distortions; hidden layer; based receiver ... See more keywords

Type-2 Fuzzy Single Hidden Layer Recurrent Neural Adaptive Terminal Super-Twisting Control of Robot Joint

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recommendations! Published in 2025 at "IEEE Transactions on Fuzzy Systems"

DOI: 10.1109/tfuzz.2025.3595677

Abstract: This research proposes a neural network-based super-twisting controller for robot joints. A modified fast nonsingular terminal sliding surface is introduced, which not only avoids singularity but also increases the convergence rate of the sliding mode… read more here.

Keywords: layer; single hidden; type fuzzy; hidden layer ... See more keywords

Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure

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recommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"

DOI: 10.1109/tnnls.2019.2919676

Abstract: In this paper, a full-regulated neural network (NN) with a double hidden layer recurrent neural network (DHLRNN) structure is designed, and an adaptive global sliding-mode controller based on the DHLRNN is proposed for a class… read more here.

Keywords: neural network; hidden layer; structure; layer ... See more keywords