Learning Fuzzy Neural Networks by Using Improved Conjugate Gradient Techniques

Abstract

One of the optimal approaches for learning a Takagi Sugeno-based fuzzy neural network model is the conjugate gradient method proposed in this research. For the PRP and the LS approaches, a novel algorithm based on the Liu-Storey (LS) approach is created to overcome the slow convergence. The developed method becomes descent and convergence by assuming some hypothesis. The numerical results show that the developed method for classifying data is more efficient than the other methods, as shown in Table (2), where the new method outperforms the others in terms of average training time, average training accuracy, average test accuracy, average training MSE, and average test MSE.