Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
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Summary
A shoulder strap retainer having a base to be positioned on the exterior shoulder portion of a garment with securing means attached to the undersurface of the base for removably securing the base to the exterior shoulders portion of the garment.
- Type
- article
- Published
- 1990-10-01
- Cited by
- 2,267
- References
- 24
- OpenAlex
- https://openalex.org/W2027197837
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13533363
Keywords
Differentiable function, Piecewise, Function approximation, Feed forward, Activation function
References
- Some applications of weighted Sobolev spaces
- Théorie des distributions
- An elasticity can be estimated consistently without a priori knowledge of functional form
- Weighted Sobolev Spaces
- Capabilities of three-layered perceptrons
- On the approximate realization of continuous mappings by neural networks
- Non-parametric estimation of econometric functionals
- Original Contribution: On learning the derivatives of an unknown mapping with multilayer feedforward networks
- Generic constraints on underspecified target trajectories
- Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions
- Multilayer feedforward networks are universal approximators
- Theory of the Back Propagation Neural Network
- Fourier approximation and embeddings of Sobolev spaces
- Probability and Measure
- Theory of the backpropagation neural network
- Multilayer feedforward networks are universal approximators
- Probability and Measure.
- Probability and Measure.
- Nonlinear signal processing using neural networks: Prediction and system modelling
- A general explanation and interrogation system for neural networks
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- Independent Research and Independent Exploratory Development FY91 Annual Report
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- Prévision de la défaillance et réseaux de neurones : l'apport des méthodes numériques de sélection de variables
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