Transceiver error reduction by design prototype system based on neural network analysis method
| Autor: | Maitham Ali Naji, Ghalib Ahmed Salman, Muthna Jasim Fadhil |
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| Rok vydání: | 2020 |
| Předmět: |
MATLAB
Control and Optimization Matching (graph theory) Computer Networks and Communications Computer science Computer Science::Neural and Evolutionary Computation Code word Initialization Data_CODINGANDINFORMATIONTHEORY Encoding (memory) Spiking neural network Self organizing feature map Electrical and Electronic Engineering Computer Science::Information Theory Forward neural network Quantitative Biology::Neurons and Cognition Artificial neural network Node (networking) Hardware and Architecture Signal Processing Encoder and decoder feature Algorithm Encoder Decoding methods Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 18:1244 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v18.i3.pp1244-1251 |
| Popis: | Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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