This paper contributes to the development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms, termed SC networks (SCNs). In contrast to the existing randomized learning algorithms for single layer feed-forward networks, we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either a constructive or selective manner. As fundamentals of SCN-based data modeling techniques, we establish some theoretical results on the universal approximation property. Three versions of SC algorithms are presented for data regression and classification problems in this paper. Simulation results concerning both data regression and classification indicate some remarkable merits of our proposed SCNs in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning, and sound generalization.

A new approach is proposed for the stability problem of delayed load frequency control (LFC) scheme with fixed and timevarying delay cases included in the current article. New stability criteria with delay dependency in terms of linear matrix inequalities for LFC systems are derived by a novel augmented Lyapunov-Krasovski (L-K) functional. Our proof deployment for system stability of power grids employs the further improved integral inequality in the form of infinite series which turns out to be less conservative than Wirtinger's inequality that encompasses Jensen inequality. Simulation case studies are carried out to show the effectiveness and superiority of the presented delaydependent PI-type LFC design scheme.