Abstract
In this paper, an AND-OR fuzzy neural network (AND-OR FNN) and a piecewise optimization approach are proposed. The in-degree of neuron and the connectivity of layer are firstly defined and Zadeh’s operators are employed in order to infer the symbolic expression of every layer, the equivalent is proved between the architecture of AND-OR FNN and fuzzy weighted Mamdani inference. The main superiority is shown not only in reducing the input space, but also auto-extracting the rule base. The optimization procedure consists of GA (Genetic Algorithm) and PA (Pruning Algorithm);the AND-OR FNN ship controller system is designed based on input-output data to validate this method. Simulating results demonstrate that the number of rule base is decreased remarkably and the performance is good, illustrate the approach is practicable, simple and effective.
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© 2006 Springer-Verlag Berlin Heidelberg
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Sui, J., Ren, G. (2006). An AND-OR Fuzzy Neural Network Ship Controller Design. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_68
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DOI: https://doi.org/10.1007/11893295_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
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