Modular Neural Networks for Pattern Classification Using LabVIEW® CS 539 Neural Networks and Fuzzy Systems Rey Hernandez CS539 Final Project Goals Description Competing Models LabVIEW® Implementation Differences From Standard Approaches Current Status 9/16/2018 University of Wisconsin - Madison

Goals Goal: To develop a pattern classifier in LabVIEW® 9/16/2018 University of Wisconsin - Madison Goals Goal: To develop a pattern classifier in LabVIEW® Pattern Classifiers normally developed with C++ or Matlab Equations used in Modular Neural Network

University of Wisconsin - Madison 9/16/2018 University of Wisconsin - Madison Description There is a strong interest in developing robust pattern classifiers that can handle an assortment of data Modular Neural networks provide that robustness by specializing in different areas of the data without sacrificing on generalizing capabilities

University of Wisconsin - Madison 9/16/2018 University of Wisconsin - Madison Competing Models Multilayer perceptrons (MLP) Preferred choice when doing pattern recognition MLPs can learn from data that is not linearly separable Weaknesses of MNNs In LabVIEW® a weakness of the MNN approach is its large memory usage

Competing Models Cont. Strengths of Modular Neural Networks (MNN) 9/16/2018 Competing Models Cont. Strengths of Modular Neural Networks (MNN) MNNs can also learn from not-linearly separable data MNNs can perform better than MLPs because they can devote each module to a separate piece of data MLPs may have difficulty with data sets that are discontinuous

University of Wisconsin - Madison 9/16/2018 University of Wisconsin - Madison LabVIEW® Popular Data Acquisition and Analysis Tool Uses a completely modular approach to programming Rather than written code, LabVIEW® uses icons and ‘wires’ to control the flow of data and computation LabVIEW® makes very efficient use of arrays which can be an asset when calculating the weights of the networks

9/16/2018 LabVIEW® cont. Modularity inherent to both LabVIEW® and Modular Neural Networks MNN can benefit from the ease of designing separate modules for simple tasks In MNN there are modules which are single layer perceptrons In LabVIEW® can make a module to behave like a perceptron network

Implementation Differences From Standard Approaches 9/16/2018 University of Wisconsin - Madison Implementation Differences From Standard Approaches Again, not written code, graphical in nature Equations can’t be handled in the usual way As in the pictorial representations of MNNs, so to does LabVIEW® provide a pictorial view of the MNN

University of Wisconsin - Madison 9/16/2018 University of Wisconsin - Madison Current Status The Program is in the initial stages right now Working version classifies nicely More can be added along the way of making a larger hierarchical structure Features to add and goals to accomplish Can classify new data but can’t save it Statistical information about the network is non-existent Ability to use a larger hierarchy would be nice