Neural Network Formalization
| Type of publication: | Idiap-RR |
| Citation: | fiesler-92.01 |
| Number: | Idiap-RR-01-1992 |
| Year: | 1992 |
| Institution: | IDIAP |
| Abstract: | In order to assist the field of neural networks in its maturing, a formalization and a solid foundation are essential. Additionally, to permit the introduction of formal proofs, it is essential to have an all encompassing formal mathematical definition of a neural network. Most neural networks, even biological ones, exhibit a layered structure. This publication shows that all neural networks can be represented as layered structures. This layeredness is therefore chosen as the basis for a formal neural network framework. This publication offers a neural network formalization consisting of a topological taxonomy, a uniform nomenclature, and an accompanying consistent mnemonic notation. Supported by this formalization, both a flexible hierarchical and a universal mathematical definition are presented. |
| Userfields: | ipdmembership={neuron learning}, |
| Keywords: | artificial neural network, connectionism, definition, formalization, mnemonic notation, neural computing, neural network statics, neurocomputing, nomenclature, standardization, terminology, topological taxonomy |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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