【摘要】：隨著社會經濟的發(fā)展,高層建筑日益增多,電梯群在高層建筑以及智能大廈中所起的作用越來越大,電梯群控系統已成為國內外研究的熱點。本文對電梯群控系統的研究主要包括兩方面內容：電梯交通模式識別和調度算法的研究,并且在研究中引入了智能控制方法。 首先,本文闡述了論文的課題背景以及研究的目的和意義,回顧了電梯群控的發(fā)展與研究現狀。 其次,本文研究了電梯群控的基本特性,主要有不確定性、擾動性、非線性和多目標性,并且給出了交通流的基本概念以及檢測交通流的方法。研究了電梯群控系統的性能評價指標,主要包括時間評價指標和能耗評價指標。研究了電梯群控系統的構成。 然后,本文研究了應用于電梯群控系統的Mamdani型模糊神經網絡,模糊神經網絡融合了模糊邏輯和人工神經網絡的優(yōu)點,易于表達知識并且有自學習能力。文中給出的Mamdani型模糊神經網絡為交通模式識別與優(yōu)化派梯提供了理論基礎。 根據給出的模糊神經網絡對電梯交通流進行模式識別。本文研究了六種典型的交通模式,詳述了各個交通模式的特征。采用三階段混合學習算法對模糊神經網絡進行學習,并結合實際交通特點采用兩個模糊神經網絡對交通流分兩步進行識別,先用網絡Ⅰ識別出上行高峰、下行高峰、空閑交通以及層間交通的比例,若層間比例較小時不需要進行網絡Ⅱ的模式識別,若層間交通比例較大時,運用網絡Ⅱ識別出兩路、四路以及隨機層間交通模式的比例。用樣本訓練模糊神經網絡,并用實際的交通流對模糊神經網絡進行測試。 最后,研究了電梯群控調度算法,電梯調度是一個典型的多目標規(guī)劃問題。本文采用前文提出的Mamdani型模糊神經網絡對電梯群進行優(yōu)化控制,控制目標選擇為平均候梯時間、平均乘梯時間、能耗。根據專家規(guī)則確定了進行優(yōu)化派梯的模糊神經網絡,采用誤差反向傳播算法對網絡進行學習。通過對實際呼梯信號的調度,進一步驗證了算法的有效性。
[Abstract]:With the development of social economy and the increasing number of high-rise buildings, elevator group plays a more and more important role in high-rise buildings and intelligent buildings. The elevator group control system has become a hot spot at home and abroad. In this paper, the research of elevator group control system mainly includes two aspects: elevator traffic pattern recognition and scheduling algorithm, and the intelligent control method is introduced in the research. First of all, this paper describes the background of the thesis, the purpose and significance of the research, and reviews the development and research status of elevator group control. Secondly, this paper studies the basic characteristics of elevator group control, including uncertainty, disturbance, nonlinearity and multi-objective, and gives the basic concept of traffic flow and the method of detecting traffic flow. The performance evaluation index of elevator group control system is studied, including time evaluation index and energy consumption evaluation index. The structure of elevator group control system is studied. Then, this paper studies the Mamdani fuzzy neural network used in elevator group control system. Fuzzy neural network combines the advantages of fuzzy logic and artificial neural network, and it is easy to express knowledge and has the ability of self-learning. The Mamdani fuzzy neural network provided in this paper provides a theoretical basis for traffic pattern recognition and optimization of ladders. According to the given fuzzy neural network, the elevator traffic flow pattern recognition is carried out. In this paper, six typical traffic modes are studied, and the characteristics of each traffic mode are described in detail. The three-stage hybrid learning algorithm is used to study the fuzzy neural network, and two fuzzy neural networks are used to identify the traffic flow in two steps according to the actual traffic characteristics. First, the uplink peak and the downlink peak are identified by network 鈪，

本文編號：2159535