Pages that link to "Item:Q1708845"

The following pages link to Multilayer feedforward networks are universal approximators (Q1708845):

Displaying 50 items.

• The essential order of approximation for nearly exponential type neural networks (Q2507488) (← links)
• Forecasting economic data with neural networks (Q2509065) (← links)
• Learning in compressed space (Q2510741) (← links)
• A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information (Q2518483) (← links)
• Universal approximation capability of EBF neural networks with arbitrary activation functions (Q2563557) (← links)
• Simultaneous \(\mathbf L^p\)-approximation order for neural networks (Q2568010) (← links)
• A new approach for neural control of nonlinear discrete dynamic systems (Q2568238) (← links)
• Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem (Q2569089) (← links)
• Path relinking and GRG for artificial neural networks (Q2570150) (← links)
• An empirical study of learning speed in back-propagation networks (Q2573009) (← links)
• From Hopfield nets to recursive networks to graph machines: numerical machine learning for structured data (Q2575085) (← links)
• The role of heterogeneous agents' past and forward time horizons in formulating computational models (Q2575446) (← links)
• On approximate learning by multi-layered feedforward circuits (Q2581366) (← links)
• Time series forecasting with multiple candidate models: selecting or combining? (Q2583096) (← links)
• High level chaos in the exchange and index markets (Q2630296) (← links)
• The financial market: not as big as you think (Q2633452) (← links)
• Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels (Q2654461) (← links)
• Machine learning based data retrieval for inverse scattering problems with incomplete data (Q2660886) (← links)
• Modeling a stochastic age-structured capital system with Poisson jumps using neural networks (Q2660900) (← links)
• Non-parametric identification of homogeneous dynamical systems (Q2665098) (← links)
• Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems (Q2671323) (← links)
• On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
• Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
• DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
• An explainable attention network for fraud detection in claims management (Q2673178) (← links)
• Recurrent neural network-based internal model control design for stable nonlinear systems (Q2673618) (← links)
• Robust satisfaction of nonlinear performance constraints using barrier-based model predictive control (Q2673629) (← links)
• Estimating Lyapunov exponents on a noisy environment by global and local Jacobian indirect algorithms (Q2673957) (← links)
• Machine learning the real discriminant locus (Q2674017) (← links)
• Isogeometric topology optimization based on deep learning (Q2674045) (← links)
• Numerical solution of the Fokker-Planck equation using physics-based mixture models (Q2674128) (← links)
• A shallow Ritz method for elliptic problems with singular sources (Q2675616) (← links)
• A discontinuity capturing shallow neural network for elliptic interface problems (Q2675625) (← links)
• Finite-time command filter-based adaptive fuzzy tracking control for stochastic nonlinear induction motors systems with unknown backlash-like hysteresis (Q2676124) (← links)
• Derivatives of feed-forward neural networks and their application in real-time market risk management (Q2676274) (← links)
• Supervised learning with a quantum classifier using multi-level systems (Q2677152) (← links)
• Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
• Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems (Q2678512) (← links)
• Deep limits of residual neural networks (Q2679108) (← links)
• Neural networks in Fréchet spaces (Q2679424) (← links)
• DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
• Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
• CPINNs: a coupled physics-informed neural networks for the closed-loop geothermal system (Q2682678) (← links)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
• A deep double Ritz method (\(\mathrm{D^2RM}\)) for solving partial differential equations using neural networks (Q2683471) (← links)
• Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization (Q2684140) (← links)
• An unfeasibility view of neural network learning (Q2685066) (← links)
• The universal approximation theorem for complex-valued neural networks (Q2689134) (← links)
• opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)