Artificial neural network training utilizing the smooth variable structure filter estimation strategy

作者：Ahmed Ryan; El Sayed Mohammed; Gadsden S Andrew^*; Tjong Jimi; Habibi Saeid

来源：Neural Computing & Applications, 2016, 27(3): 537-548.

DOI：10.1007/s00521-015-1875-2

• Neural networks
• Extended Kalman filter
• Smooth variable structure filter
• Estimation

A multilayered neural network is a multi-input, multi-output nonlinear system in which network weights can be trained by using parameter estimation algorithms. In this paper, a novel training method is proposed. This method is based on the relatively new smooth variable structure filter (SVSF) and is formulated for feed-forward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the sliding mode concept and works in a predictor-corrector fashion. The SVSF training performance is tested on three benchmark pattern classification problems. Furthermore, a study is presented comparing the popular back-propagation method, the extended Kalman filter, and the SVSF.

• 出版日期2016-4