Swarm intelligence based approach for efficient training of regressive neural networks

This work proposes an efficient approach to solve the problem of training a regressive neural network efficiently. Regressive networks are characterized by delay lines possibly in both the input and the output feedback. Each delay line is connected to the network with synaptic weights and thus increases the number of parameters that must be optimized by the training algorithm. Training algorithms such as the Levenberg–Marquardt, normally used to train neural networks, are prone to local minima entrapment, and for this reason, a strategy to initialize the training procedure correctly is needed. To solve this problem, the continuous flock of starling optimization algorithm, a highly explorative optimizer based on swarm intelligence, is used. The proposed approach is tested and validated on an experimental benchmark featuring a second-order nonlinear dynamic system.