灰色系統理論其應用範圍極廣，主要能對事物的數據不完整性與不確定性，進行關於系統的灰模型、灰預測、灰關聯分析與灰決策等。尤其是，灰模型更是灰色系統理論的核心，因此，灰模型參數值的修正與最佳化，就成為灰色系統理論的主要課題。另一方面，在灰模型的應用，研究的範疇均著重在灰模型輸出的補償，而其實現方式亦以軟體為主。事實上，在即時系統中，最關鍵的因素是系統反應時間的不一致，所以，以硬體電路實現灰模型，將是設計灰控制系統的重點。
　　本論文針對灰模型研究領域，提供四項貢獻：
　(1) 提出以灰模型為主的二階多項式迴歸模式，藉以修正與擴展灰模型的應用，實驗顯示，此修正的迴歸模型，在儀器的校正週期預測上，獲得更準確的結果。
　(2) 利用導引進化模擬退火法以獲取灰模型最佳參數值，進而將此技術應用於浮球定位控制與網路流量控制。由實驗結果，此最佳化模式，確實可以改善灰模型之準確度，且能成功應用於實際的灰預測控制系統。
　(3) 利用複雜式可規畫邏輯元件的架構，研製一個整合灰預測模組與模糊推論機之硬體電路架構，使用者只要變更知識庫的內容，就能夠實現所需控制策略的灰色預測模糊控制晶片。此硬體電路之設計與實作流程之規畫，在技術上不但可以實現灰預測模糊知識庫的線上調整與學習，在應用上亦可大幅降低在研發過程的設計成本與時程。另外，亦利用浮球定位控制系統，驗證本系統的設計流程與實際應用效能。
　(4) 基於高效率的平均值比較方式，提出高速度、多晶片特性的電壓式 WTA/MAX 電路，此設計電路適用於超大型積體電路的實作，且適合以嵌入式之應用，例如模糊控制器的 MAX/MIN 模組電路。

　　The theory of grey systems can deal with incomplete and uncertain problems by using the technologies such as grey model, grey prediction, grey relational analysis, and grey decision. Especially, the grey model occupies a very important position in the theory of grey system. Thus, to modify or optimize the parameter values of grey model, is the main subject in grey systems theory. Otherwise, in grey model applications, the research all put their emphases on determining the quantity of compensation for the output from grey model and implemented it by using software on a common purpose computer. In fact, the most critical factor in a real time system is the inconsistent response time of the system. Therefore, having the grey model running in hardware is the core of designing grey-based control system.
　　In this dissertation, we contribute four key points in the field of grey model:
　(1) Propose a grey-based two-order polynomial model to modify and extend the applications of grey model. Results demonstrate that the modified autoregressive model provide more accuracy method for forecasting the calibration interval of a measuring instrument.
　(2) By using the guided evolutionary simulated annealing (GESA) technique to search for the optimized parameter values of grey model. The applications of the technique for the ball-suspension control and network traffic congestion control are demonstrated. The results indicate that the GESA method can improve the prediction accuracy of grey model and can be applied to the scheme of grey prediction control system.
　(3) Based on complex programmable logic device (CPLD) architecture, we propose the hardware circuits of grey prediction model and fuzzy inference engine, and provide a friendly implementation flow for users to create an application specific grey prediction fuzzy controller (GPFC) by only updating the fuzzy knowledge base. The hardware implementation flow not only provide the on-line simulation and tuning for fuzzy knowledge base, but also can rapidly and effectively provide complete design automation for GPFC. Otherwise, the validity of this implementation flow is tested by applying it to a ball-suspension control system.
　(4) Based on the efficient averaged-value comparison approach, we present a voltage-mode WTA/MAX circuit that achieves high-speed and multi-chip features. This circuit is appropriate for VLSI implementation and suitable to be embedded in WTA/MAX applications such as the MAX/MIN building block of fuzzy controller.

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