Application of fuzzy T-norms towards a new Artificial Neural Networks' evaluation framework: A case from wood industry
The development of an Artificial Neural Network requires proper learning and testing procedures that adopt error correction processes and algorithms. Monitoring of processing elements values and overall performance is one of the most critical issues of an Artificial Neural Network development process. This should happen as the network evolves and it is the actual task that enables the developer to make informed decisions about the proper network topology, math functions, training times and learning parameters. This manuscript presents an innovative and flexible error validation framework applying fuzzy logic. It offers an approach capable of viewing the task of performance improvement under several different perspectives. Then the developer has the capacity to decide which performance is most suitable according to his standards. The model has been tested for a specific industrial case study with actual data and a comparison to the existing methods is presented. (c) 2008 Elsevier Inc. All rights reserved.
t-norms intelligent ann evaluation algorithms fuzzy algebra fuzzy sets