In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation namely nonlinear system identification, pattern classification and function approximation.
- Pub Date:
- May 2019
- Statistics - Machine Learning;
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning;
- Mathematics - Optimization and Control
- Circuits, Systems, and Signal Processing, vol. 36, no. 4, pp. 1639-1653, 2017