Optimization ofWavelet Neural Networks Modelby Setting theWeighted Value of OutputThrough Fuzzy Rules Takagi-Sugeno-Kang (TSK)Type As a Fixed Parameter

In this paper, we propose a model of fuzzy wavelet neural network (FWNN) which is optimized from wavelet neural network (WNN) model by setting the weighting coefficient of the output using the TSK fuzzy inference type. This coefficient in the proposed FWNN model is seen as exogenous parameters that are not updated in the learning process using the method of gradient descent with momentum. The accuracy and the execution time of the model was illustrated using several univariate time series data cases, and the results were compared with models ofWNNthat had been previously published. The simulation results for variety of these cases show that the effectiveness and the accuracy of proposed FWNN model is better than the previous model of WNN.