Pages that link to "Item:Q1708845"

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

Displaying 50 items.

• Why does deep and cheap learning work so well? (Q1676557) (← links)
• Deep learning in color: towards automated quark/gluon jet discrimination (Q1678915) (← links)
• Nonlinear fractional optimal control problems with neural network and dynamic optimization schemes (Q1687350) (← links)
• Bayesian optimization of empirical model with state-dependent stochastic forcing (Q1694078) (← links)
• Deep recurrent model for server load and performance prediction in data center (Q1694249) (← links)
• Testing for neglected nonlinearity using artificial neural networks with many randomized hidden unit activations (Q1695558) (← links)
• A machine learning approach for efficient uncertainty quantification using multiscale methods (Q1700748) (← links)
• Sketching the temperature history of geological samples: analyses of diffusion profiles using multilayer perceptrons (Q1702392) (← links)
• Multilayer feedforward networks are universal approximators (Q1708845) (← links)
• Online adaptive parameter estimation for quadrotors (Q1712008) (← links)
• Adaptive linear and normalized combination of radial basis function networks for function approximation and regression (Q1719381) (← links)
• MTN optimal tracking control of SISO nonlinear time-varying discrete-time systems without mechanism models (Q1720752) (← links)
• A review of fuzzy logic and neural network based intelligent control design for discrete-time systems (Q1723693) (← links)
• Deterministic global optimization with artificial neural networks embedded (Q1730780) (← links)
• Training artificial neural networks by a hybrid PSO-CS algorithm (Q1736659) (← links)
• Plant electrical signal classification based on waveform similarity (Q1736838) (← links)
• Adaptive tracking control for nonlinear heterogeneous multi-agent systems with unknown dynamics (Q1738560) (← links)
• Online approximate solution of HJI equation for unknown constrained-input nonlinear continuous-time systems (Q1750518) (← links)
• On the design of observer-based fuzzy adaptive controller for nonlinear systems with unknown control gain sign (Q1759748) (← links)
• Function approximation on non-Euclidean spaces (Q1763473) (← links)
• Nonlinear models for ground-level ozone forecasting (Q1766975) (← links)
• An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network (Q1770725) (← links)
• Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer (Q1770766) (← links)
• Regularized greedy algorithms for network training with data noise (Q1774153) (← links)
• Backfitting neural networks (Q1775949) (← links)
• Competitiveness of nations: a knowledge discovery examination (Q1779548) (← links)
• New study on neural networks: the essential order of approximation (Q1784569) (← links)
• Modeling the Neuman's well function by an artificial neural network for the determination of unconfined aquifer parameters (Q1787661) (← links)
• About extracting dynamic information of unknown complex systems by neural networks (Q1791118) (← links)
• Capability of artificial neural network for forward conversion of geodetic coordinates \((\phi ,\lambda ,h)\) to Cartesian coordinates \((X,Y,Z)\) (Q1798775) (← links)
• Time series-based bifurcation diagram reconstruction (Q1809178) (← links)
• Generalized one-parameter bifurcation diagram reconstruction using time series (Q1809430) (← links)
• Practical issues in temporal difference learning (Q1812929) (← links)
• Forecasting of 15 time series of DGOR with neural networks (Q1814874) (← links)
• Dynamical complexity of the Hopfield networks (Q1851431) (← links)
• Unsupervised learning in neural computation. (Q1853464) (← links)
• Novel determination of differential-equation solutions: Universal approximation method (Q1860390) (← links)
• Neural network model selection for financial time series prediction (Q1861629) (← links)
• Neural networks based subgrid scale modeling in large eddy simulations (Q1869302) (← links)
• Explaining consumer choice through neural networks: the stacked generalization approach (Q1869684) (← links)
• A comparative analysis of neural network performances in astronomical imaging (Q1873168) (← links)
• An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications (Q1876168) (← links)
• Regularising neural networks using flexible multivariate activation function (Q1877571) (← links)
• Genetic neural networks to approximate feedback Nash equilibria in dynamic games (Q1879556) (← links)
• A diffusion-neural-network for learning from small samples (Q1881156) (← links)
• Logic programs and connectionist networks (Q1884268) (← links)
• Approximation by ridge function fields over compact sets (Q1885448) (← links)
• Neural network forecasting for seasonal and trend time series (Q1887914) (← links)
• Differentiable approximation by means of the Radon transformation and its applications to neural networks (Q1891035) (← links)
• Direct training method for a continuous-time nonlinear optimal feedback controller (Q1893327) (← links)