本文提出一個『植基於遺傳演算法之模糊灰色預測控制器』(Genetic Algorithm based Fuzzy Grey Prediction Controller, GB-FGPC)的控制法則，它是結合傳統的灰色預測理論及模糊控制理論，再加上遺傳演算法則所設計的。「植基於遺傳演算法之模糊灰色預測控制器」之基本設計理念是以遺傳演算法則搜尋最佳預測步距的模糊歸屬函數，經由模糊推論機構在系統不同響應時刻對灰色預測控制器之灰色預測步距做動態調整，藉以提昇控制器之控制效果。『植基於遺傳演算法之模糊灰色預測控制器』主要是由遺傳評價機構、灰色預測器、模糊推論機構及控制決策機構四部份所組成。
為了探討『植基於遺傳演算法之模糊灰色預測控制器的控制效果，本文以比例積分控制器 (Proportional-Integral Controller, PIC)、固定預測步距之灰色預測控制器 (Grey Prediction Controller, GPC) 及本文所提出控制器，針對不同受控對象所獲得之控制效果進行比較和討論。由模擬結果，本文所提出之控制器其控制效果確實優於其他兩種型態的控制器。
本文還將『植基於遺傳演算法之模糊灰色預測控制器』實際運用於溫度控制系統上，以驗證其實用性。

In this paper, we proposed a new controller, named "Genetic Algorithm based Fuzzy Grey Prediction Controller" (GB-FGPC), which combined the conventional Grey Prediction theory. Fuzzy Control theory and Genetic Algorithm. In order to promote the control effect, this new controller used the Genetic Algorithm to search the Fuzzy membership function of the best Grey Prediction steps and used the Fuzzy reasoning mechanism to dynamically adjust the Grey Prediction steps in different response time. This new controller mainly has four parts. Genetic evaluation mechanism. Grey Prediction mechanism, Fuzzy Inference mechanism and Decision-making mechanism.
In order to investigate the effect of the Genetic Algorithm based Fuzzy Grey Prediction Controller, we used the Proportional-Integral Controller and Grey Prediction Controller with fixed predicting steps to control different plants in comparison with the new controller. After simulation, we can find that the new controller is really better than the other two controller.
For practicality, we also applied the Genetic Algorithm based Fuzzy Grey Prediction Controller to temperature-control system and got the good effect.