A modified tunneling function method for non-smooth global optimization and its application in artificial neural network

For solving a class of non-smooth unconstrained global optimization problems, we present a novel definition of the modified tunneling function which combines the characters of tunneling function and filled function, and then give a one-parameter modified tunneling function. Issues covered in the presented work include: theoretical properties, solution algorithms and numerical experiments. Furthermore, an improved artificial neural network hydrological forecasting method using the modified tunneling function is also reported. The preliminary experiment results confirm that the proposed approach is promising.