An Effective Online Sequential Stochastic Configuration Algorithm for Neural Networks

Abstract

Random Vector Functional-link (RVFL) networks, as a class of random learner models, have received careful attention from the neural network research community due to their advantages in obtaining fast learning algorithms and models, in which the hidden layer parameters are randomly generated and remain fixed during the training phase. However, its universal approximation ability may not be guaranteed if the random parameters are not properly selected in an appropriate range. Moreover, the resulting random learner’s generalization performance may seriously deteriorate once the RVFL network’s structure is not well-designed. Stochastic configuration (SC) algorithm, which incrementally constructs a universal approximator by obtaining random hidden parameters under a specified supervisory mechanism, instead of fixing the selection scope in advance and without any reference to training information, can effectively circumvent these awkward issues caused by randomness. This paper extends the SC algorithm to an online sequential version, termed as an OSSC algorithm, by means of recursive least square (RLS) technique, aiming to copy with modeling tasks where training observations are sequentially provided. Compared to the online sequential learning of RVFL networks (OS-RVFL in short), our proposed OSSC algorithm can avoid the awkward setting of certain unreasonable range for the random parameters, and can also successfully build a random learner with preferable learning and generalization capabilities. The experimental study has shown the effectiveness and advantages of our OSSC algorithm.