Comparison of T Norms and S Norms for Interval Type 2 Fuzzy Numbers in Weight Adjustment for Neural Networks
A comparison of different T norms and S norms for interval type 2 fuzzy number weights is proposed in this work. The interval type 2 fuzzy number weights are used in a neural network with an interval backpropagation learning enhanced method for weight adjustment. Results of experiments and a comparative research between traditional neural networks and the neural network with interval type 2 fuzzy number weights with different T norms and S norms are presented to demonstrate the benefits of the proposed approach. In this research, the definitions of the lower and upper interval type 2 fuzzy numbers with random initial values are presented; this interval represents the footprint of uncertainty (FOU) The proposed work is based on recent works that have considered the adaptation of weights using type 2 fuzzy numbers. To confirm the efficiency of the proposed method, a case of data prediction is applied, in particular for the Mackey Glass time series (for t 17) Noise of Gaussian type was applied to the testing data of the Mackey Glass time series to demonstrate that the neural network using a interval type 2 fuzzy numbers method achieves a lower susceptibility to noise than other methods.
Inf.