Global Exponential Stability of Learning-Based Fuzzy Networks on Time Scales
We investigate a class of fuzzy neural networks with Hebbian-type unsupervised learning on time scales. By using Lyapunov functional method, some new sufficient conditions are derived to ensure learning dynamics and exponential stability of fuzzy networks on time scales. Our results are general and...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2015/283519 |
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| Summary: | We investigate a class of fuzzy neural networks with Hebbian-type unsupervised
learning on time scales. By using Lyapunov functional method, some new sufficient conditions are
derived to ensure learning dynamics and exponential stability of fuzzy networks on time scales. Our
results are general and can include continuous-time learning-based fuzzy networks and corresponding
discrete-time analogues. Moreover, our results reveal some new learning behavior of fuzzy synapses
on time scales which are seldom discussed in the literature. |
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| ISSN: | 1085-3375 1687-0409 |