Supervised Learning in CINets

Abstract

Continuous Inference Networks (CINets), a form of multilayer fuzzy value networks allow computation with fuzzy values in concise structures, are capable of universal function approximation, and are readily interpretable through natural language, aiding maintenance modification, collaboration, and knowledge sharing. However CINets have been reliant on Subject Matter Expertise (SME) and manual tuning to realize optimal performance, limiting their applicability. With ONR support[i], ARL has developed a supervised learning process for CINets capable of designing a CINet structure, and of optimizing an existing CINet structure. The CINet supervised learning process allows the automated development of data fusion, classification, and pattern recognition structures that are interpretable modifiable, and concise. Performance of CINets developed with the supervised learning process is compared to that of Artificial Neural Network (ANNs), fuzzy logic rule set, and Bayesian network approaches.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2011
Accession Number
ADA565545

Entities

People

  • Jeffrey Weinschenk
  • Paul Bruhn

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Bayesian Networks
  • Classification
  • Computational Science
  • Computer Languages
  • Data Fusion
  • Data Sets
  • Fuzzy Logic
  • Logic
  • Machine Learning
  • Mesh Networks
  • Military Research
  • Neural Networks
  • Particle Swarm Optimization
  • Pattern Recognition
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks