Fuzzy expert systems
The ordered weighted averaging operators: theory and applications
The ordered weighted averaging operators: theory and applications
Essence of Neural Networks
Fuzzy Reasoning in Decision Making and Optimization
Fuzzy Reasoning in Decision Making and Optimization
Fuzzy Sets and Systems - Special issue: Soft decision analysis
Aggregation operators: new trends and applications
Aggregation operators: new trends and applications
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Mathematical fuzzy logic as a tool for the treatment of vague information
Information Sciences—Informatics and Computer Science: An International Journal
Median value and median interval of a fuzzy number
Information Sciences—Informatics and Computer Science: An International Journal
Information Sciences: an International Journal
Design and analysis of a fault tolerant hybrid mobile scheme
Information Sciences: an International Journal
A new fuzzy operator and its application to topology design of distributed local area networks
Information Sciences: an International Journal
Fuzzy polynomial neural networks for approximation of the compressive strength of concrete
Applied Soft Computing
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
Cancellativity properties for t-norms and t-subnorms
Information Sciences: an International Journal
Approximation capabilities of multilayer fuzzy neural networks on the set of fuzzy-valued functions
Information Sciences: an International Journal
Fuzzy random renewal reward process and its applications
Information Sciences: an International Journal
Information Sciences: an International Journal
Intelligent hybrid modelling towards the prognosis of abdominal pain
International Journal of Hybrid Intelligent Systems - CIMA-08
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Reliable probabilistic classification with neural networks
Neurocomputing
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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.