A Novel Neural-Fuzzy Guidance Law Design by Applying Different Neural Network Optimization Algorithms Alternatively for Each Step View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Jium-Ming Lin , Cheng-Hung Lin

ABSTRACT

In this research, a novel neural-fuzzy guidance law by applying different neural network optimization algorithms alternatively in each step is proposed, such as the Gradient Descent (GD), SCG (Scaled Conjugate Gradient), and Levenberg-Marquardt (LM) methods are applied to deal with those parameter variation effects as follows: target maneuverability, missile autopilot time constant, turning rate time constant and radome slope error effects. Comparing with the proportion navigation (PN) and fuzzy methods are also made; the miss distances obtained by the proposed method are lower, and the proposed acceleration commands are always without polarity changes or oscillation at the final stage. More... »

PAGES

292-301

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-05458-2_31

DOI

http://dx.doi.org/10.1007/978-3-319-05458-2_31

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011788038


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