Theory of the backpropagation neural network
Hecht-Nielsen low
The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- Deep learning in neural networks: An overview→Theory of the backpropagation neural networkLegacy edge — source detail unavailable
- Overcoming catastrophic forgetting in neural networks→Theory of the backpropagation neural networkLegacy edge — source detail unavailable
- Universal approximation of an unknown mapping and its derivatives using multilayer feedfo…→Theory of the backpropagation neural networkLegacy edge — source detail unavailable
- Wavelet networks→Theory of the backpropagation neural networkLegacy edge — source detail unavailable
- Automatic differentiation in machine learning: a survey→Theory of the backpropagation neural networkLegacy edge — source detail unavailable
- Neural Networks→Theory of the backpropagation neural networkLegacy edge — source detail unavailable
- Approximation theory of the MLP model in neural networks→Theory of the backpropagation neural networkLegacy edge — source detail unavailable
- Theory of the backpropagation neural network→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- Theory of the backpropagation neural network→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Theory of the backpropagation neural network→A massively parallel architecture for a self-organizing neural pattern recognition machineLegacy edge — source detail unavailable
- Theory of the backpropagation neural network→Bidirectional associative memoriesLegacy edge — source detail unavailable
- Universal approximation of an unknown mapping and its derivatives using multilayer feedfo…→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Bidirectional associative memories→A massively parallel architecture for a self-organizing neural pattern recognition machineLegacy edge — source detail unavailable
- Approximation theory of the MLP model in neural networks→Universal approximation of an unknown mapping and its derivatives using multilayer feedfo…Legacy edge — source detail unavailable
- Approximation theory of the MLP model in neural networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Wavelet networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Automatic differentiation in machine learning: a survey→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- Automatic differentiation in machine learning: a survey→Deep learning in neural networks: An overviewLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
Links
Topics
| Neural Networks and Applications | Computer Science |
| Advanced Memory and Neural Computing | Engineering |
| Neural Networks Stability and Synchronization | Computer Science |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports1 author record(s) attached.
- supports31 reference(s) recorded.
- supportsThe DOI's year agrees with the publication year.
- supportsA title is present.
Provenance
sha256 86a6d6715ff540a0…