Abstract
This chapter presents a hybrid differential neural network (DNN)-identifier has demonstrated excellent results even in the presence of perturbations. Convergence analysis is realized considering the practical stability of identification error for a general class of hybrid systems. As can be seen in the numerical examples this algorithm could be easily implemented. In this sense the artificial modeling strategy of the continuous subsystems could be used in the automatic control implementation.
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García, A., Chairez, I., Poznyak, A. (2009). Hybrid Differential Neural Network Identifier for Partially Uncertain Hybrid Systems. In: Yu, W. (eds) Recent Advances in Intelligent Control Systems. Springer, London. https://doi.org/10.1007/978-1-84882-548-2_7
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DOI: https://doi.org/10.1007/978-1-84882-548-2_7
Publisher Name: Springer, London
Print ISBN: 978-1-84882-547-5
Online ISBN: 978-1-84882-548-2
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