Fuzzy modeling with tolerances
Fuzzy Sets and Systems
Further contributions to the study of finite fuzzy relations equations
Fuzzy Sets and Systems
Neurocomputing
A learning algorithm of fuzzy neural networks with triangular fuzzy weights
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Identification of fuzzy relational equations by fuzzy neural networks
Fuzzy Sets and Systems
Solution algorithms for fuzzy relational equations with max-product composition
Fuzzy Sets and Systems
A Fuzzy Neuron with Binary Input and its Training Algorithm
Neural Processing Letters
A fuzzy recurrent artificial neural network (FRANN) for pattern classification
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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An iterative method of solving a set of equations based on the truncated normalized max product is described. The operation may serve as the transformation for the set of fully connected units in a fully recurrent network that might otherwise consist of linear threshold units. Because of truncation and normalization the network acting under this transformation has a finite number of states and components of the state vector are bounded. Component values however are not restricted to binary values as would be the case if the network consisted of linear threshold units but can now take on the values in the set {0,0.1,..0.9,1}. This means that each unit although still having discrete output can provide finer granularity compared to the case where a linear threshold unit is used. Truncation is natural in hardware implementation where only a finite number of places behind the decimal are retained.