Smooth statistical modeling of bivariate non-monotonic data by a three-stage LUT neural system

The present paper introduces a new statistical data modeling algorithm based on artificial neural systems. This procedure allows abstracting from datasets by working on their probability density functions. The proposed method strives to capture the overall structure of the analyzed data, exhibits competitive computational runtimes and may be applied to non-monotonic real-world data (building on a previously developed isotonic neural modeling algorithm). An outstanding feature of the proposed method is the ability to return a smoother model compared to other modeling algorithms. Smooth models could have applications in the fields of engineering and computer science. In fact, the present research was motivated by an image contour resampling problem that arises in shape analysis. The features of the proposed algorithm are illustrated and compared to the features of existing algorithms by means of numerical tests on shape resampling.