Channel Equalization Using Dynamic Fuzzy Neural Networks
| Autor: | Ming-Bin Li, Meng Joo Er |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Equalization
Quantitative Biology::Neurons and Cognition Artificial neural network Neuro-fuzzy Computer science business.industry Fuzzy neural Computer Science::Neural and Evolutionary Computation Fuzzy logic ComputingMethodologies_PATTERNRECOGNITION Recurrent neural network Bit error rate Resource allocation ComputingMethodologies_GENERAL Artificial intelligence business |
| Zdroj: | IFAC Proceedings Volumes. 41:4072-4077 |
| ISSN: | 1474-6670 |
| DOI: | 10.3182/20080706-5-kr-1001.00685 |
| Popis: | In this paper, a dynamic fuzzy neural network (DFNN) is applying for communication channel equalization problem. By combining fuzzy rules with the learning ability of neural networks, DFNN can achieve the advantages of both fuzzy logic and neural networks. The simulation results show that DFNN equalizer is superior to other equalizers such as recurrent neural network (RNN) and minimal resource allocation networks (MRAN) in terms of bit error rate (BER). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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