Stochastic configuration networks with particle swarm optimisation search

While building stochastic configuration networks (SCNs), there is no guarantee that the randomly generated weights will satisfy the supervisory mechanism and that the adopted weights will significantly reduce the training error. This paper extends SCN by applying the particle swarm optimisation (PSO) technique for one-step optimising of the set of random weights generated. These optimised weights provide a higher likelihood of satisfying the supervisory mechanism and improving the learning rate. Simulations are carried out over five regression and three classification datasets. Results demonstrate that an improved training rate and generalisation can be achieved. •Extend Stochastic Configuration Networks by improving the random weight generation.•Use particle swarm optimization to optimize the set of randomly generated weights.•Higher probability of finding weights that satisfy the supervisory mechanism.•Provide a better search, and in turn, a better training rate.•Demonstrate generalisation, and the trained model's efficiency is improved.