in this paper, we study projected non-stationary simultaneous it-erative reconstruction techniques (p-sirt). based on algorithmic op-erators, convergence result are adjusted with opial’s theorem. the advantages of p-sirt are demonstrated on examples taken from to-mographic imaging.

In this paper, we study Projected non-stationary Simultaneous It-erative Reconstruction Techniques (P-SIRT). Based on algorithmic op-erators, convergence result are adjusted with Opial’s Theorem. The advantages of P-SIRT are demonstrated on examples taken from to-mographic imaging.

A new objective function for neural net classifier design is presented, which has more free parameters than the classical objective function. An iterative minimization technique for the objective function is derived which requires the solution of multiple sets of numerically ill-conditioned linear equations. An enhanced feedforward network training algorithm is derived, which solves linear equa...

The trigonometric network, introduced in this paper, is a multilayer feedforward neural network with sinusoidal activation functions. Unlike the N-dimensional Fourier series, the basis functions of the proposed trigonometric network have no strict harmonic relationship. An effective training algorithm for the network is developed. It is shown that the trigonometric network performs better than ...