We improve the greedy algorithm which is one of the general convergence criterion for certain iterative sequence in a given space by building a constructive greedy algorithm on a normed linear space using an arithmetic average of elements. We also show the degree of approximation order is still $Ο(1sqrt{ }$) by a bounded linear functional defined on a bounded subset of a normed linear space which offers a good approximation method for neural networks.