In the current work, one of the *BPNN* methods is adopted for estimating the *OTE*_{20}. The data mining method in the present study is a multilayer *BPNN*. The *BPNN* is composed of multiple layers, and each layer has many neurons. The layers among them are interlinked with weighted coefficients. Generally, three kinds of layers occur in the *BPNN* model; the initial (first) layer signifies inputs while the middle or second (hidden) layer for computing input weights, and the last (third) layer is the output layer. The development of the *BPNN* involves three stages; the preparation of data for training is the first stage, the second stage involves various permutations and combinations of optimal network architectures, and the final third stage is testing. The number of hidden layers and neurons are selected by trial and error, and the best network topology is supposed to be that which gives very close to the desired results, i.e., after computing training error, the error is fed back to the input layer. The weighted connection (Tiwari 2006) of the input constituents is signified as is the output variable_{,} is the input variable and *y* is the number of nodes (neurons) that link to the *x*th node. characterizes bias, and shows a weighted coefficient. The *BPNN* modeling is executed through open *WEKA* software. The optimal topology of the *BPNN* model is shown in Figure 8, and its value of optimum tuning parameters is shown in Table 4.

Table 4

BPNN topology
. | Number of hidden Layers . | Momentum . | Learning rate . | Iteration . |
---|---|---|---|---|

4-9-1 | 1 | 0.2 | 0.3 | 1,500 |

BPNN topology
. | Number of hidden Layers . | Momentum . | Learning rate . | Iteration . |
---|---|---|---|---|

4-9-1 | 1 | 0.2 | 0.3 | 1,500 |

Figure 8

This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy.