Table 3

Parameters . | EPR-MOGA setting . | ||
---|---|---|---|

Case 1: Mean temperature . | Case 2: Temperature half-thickness . | Case 3: Spread of jet across the channel . | |

Regression type | Linear regression | ||

Polynomial structure | |||

Inner function type | No function | ||

Constant estimation method | Least square | ||

Range of exponents | [−2, −1.5, −1, −0.5, 0, 0.5, 1, 1.5, 2] | ||

Maximum number of terms | [1:100] | [1:30] | [1:30] |

Number of data | 1,634 | 1,632 | 34 |

Number of training data | 817 | 816 | 17 |

Number of testing data | 817 | 816 | 17 |

Input variables | (x, y, z), R, d, T _{0} | (x), R, d, T _{0} | (x,y), R, d, T _{0} |

Output variables | T | H | S |

Parameters . | EPR-MOGA setting . | ||
---|---|---|---|

Case 1: Mean temperature . | Case 2: Temperature half-thickness . | Case 3: Spread of jet across the channel . | |

Regression type | Linear regression | ||

Polynomial structure | |||

Inner function type | No function | ||

Constant estimation method | Least square | ||

Range of exponents | [−2, −1.5, −1, −0.5, 0, 0.5, 1, 1.5, 2] | ||

Maximum number of terms | [1:100] | [1:30] | [1:30] |

Number of data | 1,634 | 1,632 | 34 |

Number of training data | 817 | 816 | 17 |

Number of testing data | 817 | 816 | 17 |

Input variables | (x, y, z), R, d, T _{0} | (x), R, d, T _{0} | (x,y), R, d, T _{0} |

Output variables | T | H | S |

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