rank-order filtering

Internet Information Retrieval, Parallel Sorting, and Rank-Order Filtering Based on Dynamical Neural Circuits of Maximal Value Signal Identification Among Discrete-Time Signals

The design of mathematical models and corresponding functional block-diagrams of discrete-time neural networks for Internet information retrieval, parallel sorting, and rankorder filtering is proposed. The networks are based on the discrete-time dynamical K-winnerstake-all (KWTA) neural circuits which can identify the K largest from N input signals, where 1£ < K N is a positive integer. Implementation prospects of the networks in an up-to date digital hardware are outlined.

Rank-order filtering based on analogue k-winners-take-all neural circuit

The problem of rank-order filtering is solved on the base of analogue neural circuit which determines maximal value signals among signal set. The filter is described by system of algebra-differential equations and combines such properties as high accuracy and speed, low computational and hardware implementation complexity, and independency on initial conditions. The filter can be used for processing of constant signals, variable signals, and also equal signals. The filter simulation examples confirming theoretical statements are provided.