Article,
Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training
(2018)cite arxiv:1810.13135Comment: 14 pages and 5 figures.
Abstract
Beta Basis Function Neural Network (BBFNN) is a special kind of kernel basis
neural networks. It is a feedforward network typified by the use of beta
function as a hidden activation function. Beta is a flexible transfer function
representing richer forms than the common existing functions. As in every
network, the architecture setting as well as the learning method are two main
gauntlets faced by BBFNN. In this paper, new architecture and training
algorithm are proposed for the BBFNN. An Extreme Learning Machine (ELM) is used
as a training approach of BBFNN with the aim of quickening the training
process. The peculiarity of ELM is permitting a certain decrement of the
computing time and complexity regarding the already used BBFNN learning
algorithms such as backpropagation, OLS, etc. For the architectural design, a
recurrent structure is added to the common BBFNN architecture in order to make
it more able to deal with complex, non linear and time varying problems.
Throughout this paper, the conceived recurrent ELM-trained BBFNN is tested on a
number of tasks related to time series prediction, classification and
regression. Experimental results show noticeable achievements of the proposed
network compared to common feedforward and recurrent networks trained by ELM
and using hyperbolic tangent as activation function. These achievements are in
terms of accuracy and robustness against data breakdowns such as noise signals.
