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

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