Structure optimisation of input layer for feed-forward NARX neural network

Structure optimisation of input layer for feed-forward NARX neural network

Optimization of the Input Layer Structure for Feed-Forward Narx Neural Networks

Abstract—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 identificati...

Four-Layer Feed-Forward Neural Network with Local Glia Connections

Signal Prediction by Layered Feed - Forward Neural Network (RESEARCH NOTE).

In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...

Feed forward neural network entities

Feed Forward Neural Networks (FFNNs) are computational techniques inspired by the physiology of the brain and used in the approximation of general mappings from one nite dimensional space to another. They present a practical application of the theoretical resolution of Hilbert's 13 th problem by Kolmogorov and Lorenz, and have been used with success in a variety of applications. However, as the...