Fuzzy Perceptron Learning for Non-Linearly Separable Patterns
Perceptron learning has its wide applications in identifying interesting patterns in the large data repositories. While iterating through their learning process perceptrons update the weights, which are associated with the input data objects or data vectors. Though perceptrons exhibit their robustne...
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| Autor principal: | |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2022
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/79491 |
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| Sumario: | Perceptron learning has its wide applications in identifying interesting patterns in the large data repositories. While iterating through their learning process perceptrons update the weights, which are associated with the input data objects or data vectors. Though perceptrons exhibit their robustness in learning about interesting patterns, they perform well in identifying the linearly separable patterns only. In the real world, however, we can find overlapping patterns, where objects may associate with multiple patterns. In such situations, a clear-cut identification of patterns is not possible in a linearly separable manner. On the other hand, fuzzy-based learning has its wide applications in identifying non-linearly separable patterns. The present work attempts to experiment with the algorithms for fuzzy perceptron learning, where perceptron learning and fuzzy-based learning techniques are implemented in an interfusion manner. |
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| ISBN: | 978-1-83969-887-3 |
| DOI: | 10.5772/intechopen.101312 |