| Title: | OS3-1 Image Compression Using Hybrid Evolution Based Takagi-Sugeno Fuzzy Neural Networks |
|---|---|
| Publication: | ICAROB2017 |
| Volume: | 22 |
| Pages: | 37-40 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2017.OS3-1 |
| Author(s): | Chia-Nan Ko, Ching-I Lee |
| Publication Date: | January 19, 2017 |
| Keywords: | Takagi-Sugeno fuzzy neural networks, Quantum-behaved particle swarm optimization, Image compression, adaptive annealing learning |
| Abstract: | This article proposes a model of hybrid evolution-based Takagi-Sugeno fuzzy neural networks (TSFNNs). The model is to research and analyze how to improve the efficiency of image compression. In the proposed model, the hybrid evolution method integrates the advantages of improved quantum-behaved particle swarm optimization, adaptive annealing learning, and mutation operation to train Takagi-Sugeno fuzzy neural networks. The proposed hybrid evolution-based quantum-behaved particle swarm optimization Takagi-Sugeno fuzzy neural networks (HEQPSO-TSFNNs) can improve the coding result around boundaries to enhance the coding efficiency of the lossless compression of images. |
| PDF File: | https://alife-robotics.co.jp/members2017/icarob/data/html/data/OS_pdf/OS3/OS3-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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