Pages that link to "Item:Q1708845"

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

Displaying 50 items.

• Approximation properties of a multilayered feedforward artificial neural network (Q1895884) (← links)
• Can fuzzy neural nets approximate continuous fuzzy functions? (Q1896279) (← links)
• Fusion methods for multiple sensor systems with unknown error densities (Q1898158) (← links)
• Development of simple fitness landscapes for peptides by artificial neural filter systems (Q1899179) (← links)
• Determining functional relationships from trained neural networks (Q1900310) (← links)
• A receding-horizon regulator for nonlinear systems and a neural approximation (Q1902576) (← links)
• Neural networks, rational functions, and realization theory (Q1904427) (← links)
• Feedback linearization using neural networks (Q1905929) (← links)
• Neurocontrol: A literature survey (Q1910769) (← links)
• Nonlinear system identification and adaptive control using polynomial networks (Q1910776) (← links)
• The relative order and inverses of recurrent networks (Q1911306) (← links)
• Neural networks in the capital markets: An application to index forecasting (Q1915792) (← links)
• Linear regression versus backpropagation networks to predict quarterly stock market excess returns (Q1915795) (← links)
• A decision personal index of fuzzy numbers based on neural networks (Q1920252) (← links)
• Synchronous Boltzmann machines can be universal approximators (Q1921200) (← links)
• Chebyshev approximation by discrete superposition. Application to neural networks (Q1923884) (← links)
• Hyperbolic sigma-pi neural network operators for compactly supported continuous functions (Q1923886) (← links)
• Nonlinearity creates linear independence (Q1923888) (← links)
• A neural network controller with PID compensation for trajectory tracking of robotic manipulators (Q1925054) (← links)
• Non-linear pole-placement control with a neural network (Q1925129) (← links)
• Inflation forecasting using a neural network (Q1927765) (← links)
• Fault-tolerant nonlinear adaptive flight control using sliding mode online learning (Q1929729) (← links)
• Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks (Q1931977) (← links)
• On Clifford neurons and Clifford multi-layer perceptrons (Q1932048) (← links)
• A systematic investigation of a neural network for function approximation (Q1932102) (← links)
• Adaptive controller design for spacecraft formation flying using sliding mode controller and neural networks (Q1933450) (← links)
• A program for the Bayesian neural network in the ROOT framework (Q1943070) (← links)
• A proximal analytic center cutting plane algorithm for solving variational inequality problems (Q1952872) (← links)
• Robust adaptive neurocontrol of SISO nonlinear systems preceded by unknown deadzone (Q1954675) (← links)
• Approximation with neural networks activated by ramp sigmoids (Q1958429) (← links)
• On the probability of chaos in large dynamical systems: a Monte Carlo study. (Q1960708) (← links)
• Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks. (Q1960778) (← links)
• Approximation of functions and their derivatives: A neural network implementation with applications (Q1960876) (← links)
• Stable adaptive control with recurrent networks (Q1961186) (← links)
• Econometric methods for derivative securities and risk management (Q1969812) (← links)
• Adaptive learning of rational expectations using neural networks (Q1978600) (← links)
• Fundamental properties of pair-wise interactions naturally lead to quarks and quark confinement: a theorem motivated by neural universal approximation results (Q1979854) (← links)
• Training of deep neural networks for the generation of dynamic movement primitives (Q1982413) (← links)
• Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning (Q1986877) (← links)
• Multiscale topology optimization using neural network surrogate models (Q1986944) (← links)
• Efficient hinging hyperplanes neural network and its application in nonlinear system identification (Q1987250) (← links)
• The global optimization geometry of shallow linear neural networks (Q1988338) (← links)
• Finding the optimal shape of the leading-and-trailing car of a high-speed train using design-by-morphing (Q1990833) (← links)
• Universal approximation method for the solution of integral equations (Q1992039) (← links)
• Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling (Q1996002) (← links)
• Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning (Q1997103) (← links)
• Integral equations and machine learning (Q1997552) (← links)
• Learned sketches for frequency estimation (Q1999028) (← links)
• Application of different radial basis function networks in the illegal waste dump-surface modelling (Q1999528) (← links)
• Proposal of the ``data to equation'' algorithm (Q2000367) (← links)