Stochastic configuration networks with block increments for data modeling in process industries

Abstract Stochastic configuration networks (SCNs) that employ a supervisory mechanism to automatically and fast construct universal approximators can achieve promising performance for resolving regression problems. This paper develops an extension of the original SCNs with block increments to enhance learning efficiency, which has received considerable attention in industrial process modeling. This extension allows the learner model to add multiple hidden nodes (termed hidden node block) simultaneously to the network during construction process. To meet industrial demands, two block incremental implementations of SCNs are presented by adopting different strategies for setting the block size. Specifically, the first one adds the hidden node blocks with a fixed block size, which achieves the acceleration of convergence rate at the cost of model compactness; the second one can automatically set the block size by incorporating simulated annealing algorithm, achieving a good balance between efficiency and complexity. The two algorithms are suitable for industrial data modeling with distinct requirements on modeling speed and memory space. The improved methods for building SCNs are evaluated by two function approximations, four benchmark datasets and two real world applications in process industries. Experimental results with comparisons indicate that the proposed schemes perform favorably.