Neural Computation
Incorporating prior knowledge into networks of locally-tuned units
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
Knowledge Incorporation into Neural Networks From Fuzzy Rules
Neural Processing Letters
Fast learning in networks of locally-tuned processing units
Neural Computation
Knowledge extraction from local function networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Self-evolving neural networks for rule-based data processing
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Extending the functional equivalence of radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
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A major problem when developing neural networks or machine diagnostics situations is that no data or very little data is available for training on fault conditions. However, the domain expert often has a good idea of what to expect in terms of input and output parameter values. If the expert can express these relationships in the form of rules, this would provide a resource too valuable to ignore. Fuzzy logic is used to handle the imprecision and vagueness of natural language and provides this additional advantage to a system. This paper investigates the development of a novel knowledge insertion algorithm that explores the benefits of prestructuring RBF neural networks by using prior fuzzy domain knowledge and previous training experiences. Pre-structuring is accomplished by using fuzzy rules gained from a domain expert and using them to modify existing Radial Basis Function (RBF) networks. The benefits and novel achievements of this work enable RBF neural networks to be trained without actual data but to rely on input to output mappings defined through expert knowledge.