Feedforward neural network design with tridiagonal symmetry constraints

This paper introduces a pruning algorithm with tridiagonal symmetry constraints for feedforward neural network design. The algorithm uses a reeection transform applied to the input{hidden weight matrix in order to reduce it to its tridiagonal form. The designed FANN structures obtained by applying the proposed algorithm are compact and symmetrical. Therefore, they are well suited for eecient hardware and software implementations. Moreover, the number of the FANN parameters is reduced without a signiicant loss in performance. We illustrate the complexity and performance of the proposed algorithm by applying it as a solution to a nonlinear regression problem. We also compare the results of our proposed algorithm with those of the Optimal Brain Damage algorithm.