Fuzzy RBF Networks for Function Approximation

RBF networks face challenges in determining hidden layer node centers and basis width parameters; they possess unique optimal approximation properties without local minima. However, their Gaussian activation functions exhibit localized characteristics. We implement function approximation using fuzzy RBF networks, which effectively handle fuzzy data and uncertainty through integrated membership functions and rule-based reasoning mechanisms.