A model of parallel sorting neural network of discrete-time has been proposed. The model is described by system of difference equations and by step functions. The model is based on simplified neural circuit of discrete-time that identifies maximal/minimal values of input data and is described by difference equation and by step functions. A bound from above on a number of iterations required for reaching convergence of search process to steady state is determined. The model does not need a knowledge of change range of input data. In order to use the model a minimal difference between values of input data should be known. The network can process unknown input data with finite values, located in arbitrary unknown finite range. The network is characterized by moderate computational complexity and complexity of software implementation, any finite resolution of input data, speed,. Computing simulation results illustrating efficiency of the network are given. Keywords — Parallel sorting, neural network, difference equation, computational complexity, hardware implementation.
Design of Parallel Sorting System Using Discrete-Time Neural Circuit Model