本文選題：突水預測 + 信息融合��； 參考：《遼寧工程技術大學》2013年碩士論文

【摘要】：煤礦水害事故的頻發(fā)，給煤礦工人的生命安全帶來隱患的同時煤礦的經濟效益也因水害受到了極大的威脅。其中，造成最嚴重危害的當屬礦井底板突水事故。由于我國煤礦特殊的水文地質條件,造成底板突水的影響因素多而且關系復雜，許多傳統(tǒng)的礦井底板突水預測方法已經不能夠在實際中達到理想的預測效果。 面對仍不完善的礦井底板突水預測法，急需新的技術理論融入其中，本文就此提出了一種雙層信息融合技術，將其應用于礦井底板突水預測中，并設計了實現(xiàn)突水預測的方案。首先，介紹了底板突水預測和信息融合技術的研究現(xiàn)狀；其次論述了多源信息融合的原理、層次結構和融合算法等，接著主要研究了基于RBF神經網絡的信息融合方法，將影響礦井底板突水的多個傳感器采集的數(shù)據經過處理后作為RBF神經網絡的輸入，通過分析選擇量子粒子群智能算法對網絡各個參數(shù)值進行優(yōu)化，建立了基于RBF神經網絡特征層信息融合的礦井底板突水預測方法。單一采用特征層信息融合的結果存在一定的不穩(wěn)定性，為了提高突水預測的可靠性，引入了D-S證據理論進行決策層信息融合，把每一個神經網絡的輸出歸一化處理后轉變?yōu)楦鱾�證據體的基本概率分配函數(shù)，再利用融合規(guī)則對各個證據體進行融合，根據決策規(guī)則判決，得出最后決策結果。 本文將信息融合技術引入礦井底板突水預測中，采用了雙層信息融合算法，建立了礦井底板突水預測的一般框架。經實驗分析，雙層信息融合方法所計算出的預測結果準確性高、不確定性低，在礦井底板預測領域有很好的應用前景。
[Abstract]:The frequent occurrence of mine water hazard accidents brings hidden trouble to the life safety of coal miners. Meanwhile, the economic benefits of coal mines are also greatly threatened by water hazards. Among them, the most serious harm is the mine floor water inrush accident. Because of the special hydrogeological conditions of coal mine in our country, there are many factors affecting the water inrush from the floor and the relationship is complex. Many traditional prediction methods of water inrush from the floor of the mine can no longer achieve the ideal prediction effect in practice. In the face of the imperfect prediction method of mine floor water inrush, it is urgent to incorporate new technical theory into it. In this paper, a two-layer information fusion technique is put forward, which is applied to the prediction of mine floor water inrush, and a scheme to realize the prediction of water inrush is designed. Firstly, this paper introduces the research status of water inrush prediction and information fusion technology of bottom plate, then discusses the principle, hierarchical structure and fusion algorithm of multi-source information fusion, and then mainly studies the information fusion method based on RBF neural network. The data collected by several sensors which affect the water inrush of the mine floor are processed as the input of the RBF neural network, and the parameters of the network are optimized by analyzing and selecting the quantum particle swarm intelligence algorithm. A prediction method of mine floor water inrush based on RBF neural network feature layer information fusion is established. In order to improve the reliability of water inrush prediction, the D-S evidence theory is introduced to fuse the information of decision level in order to improve the reliability of water inrush prediction. The output of each neural network is normalized and transformed into the basic probability distribution function of each evidence body, and then the fusion rules are used to fuse each evidence body, and the final decision result is obtained according to the decision rule. In this paper, the information fusion technology is introduced into the prediction of water inrush from mine floor, and a general frame of prediction of water inrush from mine floor is established by using the two-layer information fusion algorithm. The experimental results show that the prediction results calculated by the two-layer information fusion method have high accuracy and low uncertainty. It has a good application prospect in the field of mine floor prediction.
【學位授予單位】：遼寧工程技術大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TD745;TP202

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