optimization-of-the-input-layer-structure-for-feed-forward-narx-neural-networks.pdf (247.22 kB)
Download fileOptimization of the input layer structure for feed-forward NARX neural network
This paper presents an optimization method for
reducing the number of input channels and the complexity of the
feed-forward NARX neural network (NN) without compromising the
accuracy of the NN model. By utilizing the correlation analysis
method, the most significant regressors are selected to form the input
layer of the NN structure. An application of vehicle dynamic model
identification is also presented in this paper to demonstrate the
optimization technique and the optimal input layer structure and the
optimal number of neurons for the neural network is investigated.
Funding
This work was supported by Jaguar Land Rover and the UK-EPSRC grant EP/xxxxxxx/x as part of the jointly funded Programme for Simulation Innovation.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering