Harder efforts are required for visualizing the learning process. This comes from the existence of several layers in the network Advantage and disadvantage of RBFN RBF trains faster than a MLP Another advantage that is claimed is that the hidden layer is easier to interpret than the hidden layer in an MLP. Although the RBF is quick to train, when training is finished and it is being used it is slower than a MLP, so where speed is a factor a MLP may be more appropriate. T-Norm Operator:- T-norm (also T-norm or, unabbreviated, triangular norm) is a kind of used in the framework of and in , specifically in . A t-norm generalizes in a and in . The name triangular norm refers to the fact that in the framework of probabilistic metric spaces t-norms are used to generalize of ordinary . Definition :- T-norm is a T: [0, 1] × [0, 1] [0, 1] which satisfies the following → properties: : T(a, b) = T(b, a) : T(a, b) d) if a c and b d ≤ T(c, ≤ ≤
Harder efforts are required for visualizing the learning process. This comes from the existence of several layers in the network Advantage and disadvantage of RBFN RBF trains faster than a MLP Another advantage that is claimed is that the hidden layer is easier to interpret than the hidden layer in an MLP. Although the RBF is quick to train, when training is finished and it is being used it is slower than a MLP, so where speed is a factor a MLP may be more appropriate. T-Norm Operator:- T-norm (also T-norm or, unabbreviated, triangular norm) is a kind of used in the framework of and in , specifically in . A t-norm generalizes in a and in . The name triangular norm refers to the fact that in the framework of probabilistic metric spaces t-norms are used to generalize of ordinary . Definition :- T-norm is a T: [0, 1] × [0, 1] → [0, 1] which satisfies the following properties: : T(a, b) = T(b, a) : T(a, b) ≤ T(c, d) if a ≤ c and b ≤ d
46797874-Soft-Computing.docx