Convergence analysis of the batch gradient-based neuro-fuzzy learning algorithm with smoothing L1/2 regularization for the first-order Takagi-Sugeno system

It has been proven that Takagi-Sugeno systems are universal approximators, and they are applied widely to classification and regression problems. The main challenges of these models are convergence analysis and their computational complexity due to the large number of connections and the pruning of unnecessary parameters. The neuro-fuzzy learning algorithm involves two tasks: generating comparable sparse networks and training the parameters. In addition, regularization methods have attracted increasing attention for network pruning, particularly the Lq (0