Presentation | 1993/9/22 On the realization of automata by recurrent higher order neural networks Ken Tanaka, Itsuo Kumazawa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper we will discuss about the learning of the formal language by Recurrent Higher Order Neural Networks.First,we will survey some past studies and point out an primal difficulty which exists in learning process.Secondly,we will apply a neural network model that includes higher order connection weights.In addition,we will show the ability of our model to solve the problem which lies in the past models from the point of view of the ability to realize any state transfer function.Thirdly,we will derive the typical learning algorithm using the gradient descent method.Last of all,to make the efficiency of our model clear,we will show some computer simulation results applying some examples of the regular language. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | higher order connections / recurrent neural networks / regular language / Automaton |
Paper # | NC93-33 |
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Committee | NC |
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Conference Date | 1993/9/22(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | On the realization of automata by recurrent higher order neural networks |
Sub Title (in English) | |
Keyword(1) | higher order connections |
Keyword(2) | recurrent neural networks |
Keyword(3) | regular language |
Keyword(4) | Automaton |
1st Author's Name | Ken Tanaka |
1st Author's Affiliation | Department of Computer,Faculty of Engineering,Science,Tokyo Institute of Technology() |
2nd Author's Name | Itsuo Kumazawa |
2nd Author's Affiliation | Department of Computer,Faculty of Engineering,Science,Tokyo Institute of Technology |
Date | 1993/9/22 |
Paper # | NC93-33 |
Volume (vol) | vol.93 |
Number (no) | 247 |
Page | pp.pp.- |
#Pages | 8 |
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