Training feedforward networks using simultaneous perturbation with dynamic tunneling
An efficient technique, namely simultaneous perturbation with dynamic tunneling for training single hidden layer feedforward network, is proposed. A sigmoidal hidden neuron is added to the single hidden layer neural network after training. Then the cascaded network is trained again using simultaneous perturbation. The dynamic tunneling technique is employed to detrap the local minima in training. The proposed technique is shown to give better convergence results for the selected problems, namely neuro-controller, encoder, adder, demultiplexer, XOR and LT character recognition problem.
simultaneous perturbation dynamic tunneling single hidden layer cascade