High-speed learning algorithm for constructive granular systems

Conventional gradient descent learning algorithms for soft computing systems have a learning speed bottleneck problem and the local minima problem. To effectively solve the two problems, the n-variable constructive granular system with high-speed granular constructive learning is proposed based on granular computing and soft computing. The new anular constructive learning algorithm can highly speed up granular knowledge discovery by directly calculating all parameters of the n-variable constructive granular system using training data, and then constructs the n-variable constructive granular system using a small number of granular rules. Simulation results indicate that the direct calculation-based granular constructive algorithm is useful in terms of learning speed, learning error and prediction error