Randomized Neural Networks, Kernel Ridge Regression and Non-iterative Learning

This page presents our research centered around randomized neural networks, kernel ridge regression and related topics in the area of non-iterative learning.

1.    L. Zhang, P. N. Suganthan, "A Comprehensive Evaluation of Random Vector Functional Link Networks," Information Sciences, DOI: 10.1016/j.ins.2015.09.025.  Software Available here.

2.     Y. Ren, P.  N. Suganthan, N. Srikanth, G. Amaratunga, "Random Vector Functional Link Network for Short-term Electricity Load Demand Forecasting", Information Sciences, Software Available here.   Also from: https://github.com/ron1818/PhD_code/

In the context of time series forecasting, the above work shows that the direct links in RVFL improve the performance in a statistically significant manner. The direct links can be compared to the time delayed line in the finite impulse response filter (FIR). Randomization range can also be tuned to improve performance. RVFL with direct input-output links slightly outperformed SVR also. 18 time series datasets were used in this study.

 

3.      L. Zhang, P. N. Suganthan, "A Survey of Randomized Algorithms for Training Neural Networks," Information Sciences, DoI: 10.1016/j.ins.2016.01.039, accepted, 2016.

 Promising Future Research Direction: There is an increase in research activities using randomized neural networks. Researchers are encouraged to further investigate the role of direct links in RVFL and other randomized neural network configurations.

 

The above 3 papers are identified as HOT papers and/or highly cited papers by Science Citation Index (or Web of Science) as of 15th Feb 2019.

 

 Related Special Issues and Special Sessions:

1.      Applied Soft Computing Special Issue on Non-iterative Learning methods. Expected publication in early 2018.

2.      IJCNN 2018 special session on non-iterative learning.

3.      Cognitive Computation journal special issue on non-iterative learning.

4.      Pattern Recognition Journal Special Issue on Ensemble Deep Learning