Backpropagation to train an evolving radial basis function neural network

Abstract

In this paper, a stable backpropagation algorithm is used to train an online evolving radial basis function neural network. Structure and parameters learning are updated at the same time in our algorithm, we do not make difference in structure learning and parameters learning. It generates groups with an online clustering. The center is updated to achieve the center is near to the incoming data in each iteration, so the algorithm does not need to generate a new neuron in each iteration, i.e., the algorithm does not generate many neurons and it does not need to prune the neurons. We give a time varying learning rate for backpropagation training in the parameters. We prove the stability of the proposed algorithm.

Dynamics Model Abstraction Scheme Using Radial Basis Functions

“…The inputoutput relationship is in accordance with the equations of motion. In this work, we propose and show the RBF to be useful in approximating the unknown nonlinearities of the dynamical systems [41,42,[50][51][52][53][54][55][56] through the control signal (4) found for the estimation method for the dynamics of the joints of a robot.…”