Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.
D. C. Wunsch et al., "Query-Based Learning for Aerospace Applications," IEEE Transactions on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at http://dx.doi.org/10.1109/TNN.2003.820826
Electrical and Computer Engineering
Keywords and Phrases
Kalman Filters; Aerospace Application; Aerospace Computing; Aerospace Control; Causality Index; Continuous Network Inversion; Control Distribution; Discrete Network Inversion; Emergency Egress Safety; Feedforward Neural Nets; Heuristic Programming; Input Search Dimensionality; Learning (Artificial Intelligence); Multilayer Perceptrons; Neural Network Training; Node Decoupled Extended Kalman Filter; Oracle Query; Original Heuristic; Pattern Recognition; Query-Based Learning
International Standard Serial Number (ISSN)
Article - Journal
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