基于RS-FNN的電梯群智能控制策略

在電梯群控制調(diào)度算法中，如何有效利用電梯群控制系統(tǒng)中的大量復(fù)雜、冗余或不完
備的原始數(shù)據(jù)以及群控制策略如何適應(yīng)多變的電梯交通狀況是提高電梯群控性能的關(guān)鍵問題。本文提出一種新型的粗糙集－模糊神經(jīng)網(wǎng)絡(luò)混合控制策略。針對電梯群控制系統(tǒng)中大量可參考屬性信息，采用粗糙集理論進行屬性約簡，提煉出在不同交通流模式下對系統(tǒng)最重要的屬性，再建立相應(yīng)交通流模式下的模糊神經(jīng)網(wǎng)絡(luò)。仿真實驗證明該群控制策略在派梯調(diào)度時，可實時預(yù)測和辨識電梯交通流模式，根據(jù)不同交通流模式采用不同的模糊神經(jīng)網(wǎng)絡(luò)模型進行派梯計算，尤其提高了電梯群控系統(tǒng)在混合交通流狀況下的運行效率。
關(guān)鍵詞:電梯群控系統(tǒng)；粗糙集；模糊神經(jīng)網(wǎng)絡(luò)
Abstract: Effective mining the huge elevator data in elevator group control system and improve the performance in variable traffic pattern is the key problem in elevator group control policy research. In this paper, a novel intelligent elevator group control strategy based on rough set theory and fuzzy neural network called RS-FNN strategy is proposed. As a strong data fusion method, rough set theory can extract the most important system attributes. This proposed control method firstly reduce the main attributes in elevator group control system with discernibility matrix method in rough set theory, and then build up a fuzzy neural network to control the elevator group. The simulation results show that this proposed novel elevator group control method can improve elevator system performance and adapt complicated traffic flow in variable elevator traffic flow conditions.
Keywords: Elevator group control system; rough set, fuzzy neural network