In this paper an adaptive learning algorithm for B spline networks is presented

in which the number of B spline functions in the hidden layer is automatically determined from the information contained in training pairs and both weights and interior knot positions corresponding to the non zero B spline basis functions are iteratively adjusted by the gradient descent rule.Computer simulation results show that the proposed algorithm is more efficient and feasible than the existing learning algorithm used in B spline networks.