Global optimization algorithms for training product unit neural networks

Product units in the hidden layer of multilayer neural networks provide a powerful mechanism for neural networks to efficiently learn higher-order combinations of inputs. Training product unit networks using local optimization algorithms is difficult due to an increased number of local minima and increased chances of network paralysis. The paper discusses the problems with using gradient descent to train product unit neural networks, and shows that particle swarm optimization, genetic algorithms and LeapFrog are efficient alternatives to successfully train product unit neural networks.