A further study on the inequality constraints in stochastic configuration networks

Abstract Stochastic Configuration Networks (SCNs) can be incrementally constructed by using supervisory mechanisms on the selection of random weights and biases. Due to its ease in implementation, fast training and less human intervention, SCNs become increasingly popular for large-scale data analytics. This paper aims to further study the existing constraint condition used in building SCNs. Two new inequality constraints on random parameters assignment are presented, and a theoretical guidance for the key parameter selection in these constraints is given. The newly proposed inequality constraints enlarge the probability of the constraint holding, which implies a quicker learning process. Experimental results with comparisons indicate that the proposed constraints in this paper can greatly reduce the search time for constructing the hidden nodes.