On-line learning of RVFL neural networks on finite precision hardware

In this paper, a new algorithm for on-line learning of Random Vector Functional-Link neural network is proposed. It is specifically tailored to hardware implementations with finite precision arithmetic in distributed computing scenarios, where the massive use of low-cost hardware resources for sensor networks or computing agents is necessary in order to deal with big data, IoT paradigms and multiple sources of information. The proposed algorithm does not require any specific DSP operation to be implemented in hardware, like matrix inversions or multiplications, for real-time learning. However, experimental results prove that the algorithm outperforms commonly adopted recursive least-squares algorithms optimized for hardware implementation, while the loss of performance with respect to batch training is reduced, as the proposed approach is proved to be very efficient when a reduced number of bits is adopted for finite precision hardware implementation.