This proposal presents a novel use of Weightless Neural Networks (WNN) and Steinbuch Lernmatrix for pattern recognition and classification. High speed of learning, easy of implementation and flexibility given by WNN, combined with the learning capacity, recovery efficiency, noise immunity and fast processing shown by Steinbuch Lernmatrix are key factors considered on the pattern recognition exposed by the suggested model. For experimental purposes, the fundamental pattern sets are built and provided to the model under the learning phase. The additive, subtractive and mixed noises are applied to fundamental patterns to check out the response of the model during the recovery phase.
Field Programmable Gate arrays are used in the implementation of such model, since it allows custom user-defined models to be embedded in a reconfigurable hardware platform, and provides block memories and dedicated multipliers suitable for the model.
This article introduces a new theoretical framework to describe the behavior of the Steinbuch's Lernmatrix. The properties of this old associative memory can be modeled using set theory and order relationships, analogously to morphological associative memories. The obtained results allow the Lernmatrix, four decades before its creation, to be a good alternative for pattern classification and recognition.
KEYWORDS: Process modeling, Process control, Systems modeling, Data modeling, Error analysis, Control systems, Complex systems, Sensors, Signal processing, Mathematics
In this work, we are proposing a model that improves closed loop automatic controllers for real time processes, using only a positive function to deal with the process variations, no matter if they are positive or negative, the unit uses the sign to deal with the control output value. The first part of this document, gives an introduction to the kind of control systems that can be applied, and the reason to make this project. The second part gives the parameters to be used on the control model, as well as the signal conditioning stage. Then at third part, the design of the model is explained, followed by Results and conclusions at fourth and fifth parts respectively.
Conference Committee Involvement (1)
Mathematical Methods in Pattern and Image Analysis
3 August 2005 | San Diego, California, United States
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