94,070 research outputs found
Hybrid expert system of rough set and neural network
The combination of neural network and expert system can accelerate the process of inference, and then rapidly produce associated facts and consequences. However, neural network still has some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. Thus to address these issues we develop a new method for pre-processing based on rough set and merge it with neural network and expert system. The resulting system is a hybrid expert system model called a Hybrid Rough Neural Expert System (HRNES)
Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge
This work presents an online learning-based control method for improved
trajectory tracking of unmanned aerial vehicles using both deep learning and
expert knowledge. The proposed method does not require the exact model of the
system to be controlled, and it is robust against variations in system dynamics
as well as operational uncertainties. The learning is divided into two phases:
offline (pre-)training and online (post-)training. In the former, a
conventional controller performs a set of trajectories and, based on the
input-output dataset, the deep neural network (DNN)-based controller is
trained. In the latter, the trained DNN, which mimics the conventional
controller, controls the system. Unlike the existing papers in the literature,
the network is still being trained for different sets of trajectories which are
not used in the training phase of DNN. Thanks to the rule-base, which contains
the expert knowledge, the proposed framework learns the system dynamics and
operational uncertainties in real-time. The experimental results show that the
proposed online learning-based approach gives better trajectory tracking
performance when compared to the only offline trained network.Comment: corrected version accepted for ICRA 201
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