Abstract
This paper presents the evolutive learning for the newly proposed Generalized Fuzzy Model (GFM), which combines the inherent features of the existing Compositional Rule of Inference (CRI) and Takagi-Sugeno (TS) models. The evolutive learning is necessitated by the fact that local learning involving the hybridization of LSE and Gradient Descent (GD) techniques fails to yield the targeted performance for certain dynamic systems. The LSE is used to estimate the consequent part, and GD is used to estimate the premise part of the IF-THEN fuzzy rule. Further learning by the hybrids of Genetic and Simulated Annealing techniques, known as GA hybrid and SA hybrid, provides improved performance. The results are demonstrated on stock market data.