Tham khảo tài liệu 'chaotic systems part 2', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 14 Chaotic Systems Fig. 10. Time series of the observed value network targets and the predicted value network outputs for the 5-min traffic volume. 10-min traffic volume The network inputs and targets are the 14-dimensional delay coordinates x i x i-10 x i-20 . x i-130 and x i 1 respectively. Similarly by using Bayesian regularization the effective number of parameters is first found to be 108 as shown in Fig. 11 therefore the appropriate number of neurons in the hidden layer is 7 one half of the number of elements in the input vector . Replace the number of neurons in the hidden layer with 7 and train the network again. The training process stops at 11 epochs because the validation error has increased for 5 iterations. Fig. 12 shows the scatter plot for the training set with correlation coefficient p . Simulate the trained network with the prediction set. Fig. 13 shows the scatter plot for the prediction set with the correlation coefficient p . Time series of the observed value network targets and the predicted value network outputs are shown in Fig. 14. If the strategy early stopping is disregarded and 100 epochs is chosen for the training process the performance of the network improves for the training set but gets worse for the validation and prediction sets. If the number of neurons in the hidden layer is increased to 14 and 28 the performance of the network for the training set tends to improve but does not have the tendency to improve for the validation and prediction sets as listed in Table 4. No. of Neurons Data 7 14 28 Training Set Validation Set Prediction Set Table 4. Correlation coefficients for training validation and prediction data sets with the number of neurons in the hidden layer increasing 10-min traffic volume . Short-Term Chaotic Time Series Forecast 15 Fig. 11. The convergence process to find effective number of parameters used by the network for the .
