  In this paper, a novel stepwise learning approach based on estimating desired
premise parts' outputs by solving a constrained optimization problem is
proposed. This learning approach does not require backpropagating the output
error to learn the premise parts' parameters. Instead, the near best output
values of the rules premise parts are estimated and their parameters are
changed to reduce the error between current premise parts' outputs and the
estimated desired ones. Therefore, the proposed learning method avoids error
backpropagation, which lead to vanishing gradient and consequently getting
stuck in a local optimum. The proposed method does not need any initialization
method. This learning method is utilized to train a new Takagi-Sugeno-Kang
(TSK) Fuzzy Neural Network with correlated fuzzy rules including many
parameters in both premise and consequent parts, avoiding getting stuck in a
local optimum due to vanishing gradient. To learn the proposed network
parameters, first, a constrained optimization problem is introduced and solved
to estimate the desired values of premise parts' output values. Next, the error
between these values and the current ones is utilized to adapt the premise
parts' parameters based on the gradient-descent (GD) approach. Afterward, the
error between the desired and network's outputs is used to learn consequent
parts' parameters by the GD method. The proposed paradigm is successfully
applied to real-world time-series prediction and regression problems. According
to experimental results, its performance outperforms other methods with a more
parsimonious structure.

Machine Learning;Artificial Intelligence