  The theory of random vector functional link network (RVFLN) has provided a
breakthrough in the design of neural networks (NNs) since it conveys solid
theoretical justification of randomized learning. Existing works in RVFLN are
hardly scalable for data stream analytics because they are inherent to the
issue of complexity as a result of the absence of structural learning
scenarios. A novel class of RVLFN, namely parsimonious random vector functional
link network (pRVFLN), is proposed in this paper. pRVFLN features an open
structure paradigm where its network structure can be built from scratch and
can be automatically generated in accordance with degree of nonlinearity and
time-varying property of system being modelled. pRVFLN is equipped with
complexity reduction scenarios where inconsequential hidden nodes can be pruned
and input features can be dynamically selected. pRVFLN puts into perspective an
online active learning mechanism which expedites the training process and
relieves operator labelling efforts. In addition, pRVFLN introduces a
non-parametric type of hidden node, developed using an interval-valued data
cloud. The hidden node completely reflects the real data distribution and is
not constrained by a specific shape of the cluster. All learning procedures of
pRVFLN follow a strictly single-pass learning mode, which is applicable for an
online real-time deployment. The efficacy of pRVFLN was rigorously validated
through numerous simulations and comparisons with state-of-the art algorithms
where it produced the most encouraging numerical results. Furthermore, the
robustness of pRVFLN was investigated and a new conclusion is made to the scope
of random parameters where it plays vital role to the success of randomized
learning.
