Gradient Descent Methods for Type-2 Fuzzy Neural Networks
Abstract
Given an initial point, if an algorithm tries to follow the negative of the gradient of the function at the current point to reach a local minimum, we face the most common iterative method to optimize a nonlinear function: the GD method. The main goal of this chapter is to briefly discuss a multivariate optimization technique, namely the GD algorithm, to optimize a nonlinear unconstrained problem. The referred optimization problem is a cost function of a FNN, either type-1 or type-2, in this chapter. The main features, drawbacks and stability conditions of these algorithms are discussed.
Keywords
Gradient descent for FNN
Levenberg-Marquardt for FNN
Momentum-term ...
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