Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab
Computers and Industrial Engineering
An adaptive neuro-fuzzy system for efficient implementations
Information Sciences: an International Journal
Multivariate stochastic fuzzy forecasting models
Expert Systems with Applications: An International Journal
A fuzzy neural network with fuzzy impact grades
Neurocomputing
Expert Systems with Applications: An International Journal
Evaluating direction-of-change forecasting: Neurofuzzy models vs. neural networks
Mathematical and Computer Modelling: An International Journal
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Multiple DNN identifier for uncertain nonlinear systems based on Takagi-Sugeno inference
Fuzzy Sets and Systems
Information Sciences: an International Journal
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An architecture for neural networks that can handle fuzzy input vectors is proposed, and learning algorithms that utilize fuzzy if-then rules as well as numerical data in neural network learning for classification problems and for fuzzy control problems are derived. The learning algorithms can be viewed as an extension of the backpropagation algorithm to the case of fuzzy input vectors and fuzzy target outputs. Using the proposed methods, linguistic knowledge from human experts represented by fuzzy if-then rules and numerical data from measuring instruments can be integrated into a single information processing system (classification system or fuzzy control system). It is shown that the scheme works well for simple examples