基于复杂系统模型的地下采矿无线传感器网络中的优化混合神经网络
发布时间：2018-11-09 09:50
【摘要】：地下采矿是全球最危险的工作之一,在过去的二十年,在诸如火灾、落石、洪水、有毒气体等重大事故中均有重大人员伤亡和巨大的生命和财产损失。生命的伤亡和财产的巨大损失破坏了社会的稳定和可持续发展。政府、采矿业、工程师、科学家和计算机专家提出的各种有效的措施正在缓解情况。这些措施包括用以预测安全和采取救援措施的地下挖掘模型,以便在危险的时候通过监测来帮助跟踪和营救矿工。 然而,大部分用以分析疏散问题和沟通的先进计算机模型通常运算起来非常耗时的。例如,包含额外的平方运算但获得了全球公认的高斯径向基模型,随着网络规模变大,将比其他没有平方运算的模型更需要时间。本文侧重于优化路由或传输路径(R),事故地点被构建为纯粹的随机事件,通过建立神经网络模型对特定岩层信息来估算信息链没有损坏的概率。这样,当真正的事故发生时,机器人通过存储记忆的复杂关系的神经网络,运用实时看到的岩层信息就能够立刻预测信息链没有损坏概率。如果预测结果是积极的,机器人等待接收救援信号；否则,它将进入更深岩层,重复这个过程。 文章第二部分通过改进已有文献中S函数和紧凑R函数方法,给出了用绝对值运算代替平方运算来减少高斯模型运算负担的新模型,进而降低计算成本和提高运行效率。 最后,基于这一模型引入一系列混合神经网络。为了减少学习过程中的错误以及分析其他优化参数,采用了对混合神经网络中的转移函数引入了线性和非线性加权等措施。 大脑具有由神经系统控制的故障识别功能和自愈合机制；本文试图考虑到两个神经功能共同参导时混合模型有效性,尤其为地下矿难救援行动提出了一个独特的模型,来进一步减少大脑的工作。 本文研究合理性在于将目前的研究焦点从小世界网络的系统分析转移到数以百万计节点的网络,这将要求计算机具有高处理能力并且要进行相当长的时间运行。因此,对大量的传感器信息处理的快速计算算法的需要已经成为当务之急,更不用说基本数据有可能在事故中可能被毁掉。此外,简单的模仿人类大脑的神经网络,可以演示以前需要人类专家来参与的快速学习和准确处理分类属性问题。尽管这些工具显然无法取代人类专家,但他们为问题诊断和决策支持提供了依据。 自适应变异粒子群优化(A MPSO)f(?)编码的遗传算法用于更新基础转移函数,这使得修正后的函数可以加快训练过程,提高神经网络的学习精度。 从基本模型获得的结果表明,CRBF模型在各参数(平均迭代次数、收敛时间、标准方差误差和计算时间)表现更优；在采用PSO算法训练的模型中,最优误差分别为CRBF模型(0.0111)、ZRBF(0.0140) S函数模型(0.0157)、高斯算法(0.0120),而采用遗传算法训练的模型中,CRBF, SBF, ZRBF和GRBF的最优误差值分别是0.012923,0.0126,0.012183,0.12291。相对于目标的最优误差0.01而言,在采用PSO算法和遗传算法训练负余弦中非线性加权混合模型中合模型中,最佳的优化误差分别为0.009和0.01109；其次是在采用PSO算法和遗传算法训练线性混合模型中,最优误差是0.0103,在非线性负余弦加权g-比例混合模型中,最优误差是0.011。 从结果可以看出,PSO算法模型被证明是地下矿山、隧道和其他自然(如滑坡)救援行动中的强有力的选择。遗传算法模型,特别是SBF训练良好,但在岩石渗透是有困难并且容易出错,但是在诸如地表采矿、医院和建筑物等的疏散行动中非常有效。 本文分为五个章节。第1章讨论了复杂的自适应系统的背景、目标、意义,并提出模型的概念框架。第二章着重介绍一些相关文献,包括S函数和径向基函数,以及研究这些函数所用的方法。第三章对路由路径生成提出假设,并构建了基本模型。第四章探讨了已提出模型的几种混合情况,包括线性和非线性加权混合模型及其分析。第五章对于已提出的遗传算法的路由路径进行了分析,并讨论了模型在粒子群和遗传算法中的应用趋势。最后,第六章是总结和拟进一步开展的研究工作。
[Abstract]:Underground mining is one of the most dangerous work in the world. In the past two decades, there are major casualties and great loss of life and property in major accidents, such as fire, rockfall, flood, and toxic gas. The loss of life and the loss of property damage the stability and sustainable development of society. The various effective measures proposed by the Government, the mining industry, the engineers, scientists and computer experts are being mitigated. These include an underground mining model to predict safety and to take rescue measures in order to help track and rescue miners at risk through monitoring. However, most of the advanced computer models used to analyze evacuation problems and communication are typically operational When, for example, an additional square operation is included, a globally accepted Gaussian radial basis model is obtained, and as the network scale becomes large, there will be a need for a model that does not have a square operation Time. This paper focuses on the optimization of routing or transmission path (R). The accident site is constructed as a pure random event, and the information chain is estimated to be free from damage by establishing a neural network model. The probability is that, when the real accident happens, the robot can predict the information chain without damage immediately by using the neural network of the complex relation of memory and memory, Probability. If the prediction is positive, the robot waits to receive a rescue signal; otherwise it will go deeper into the formation and repeat this in that second part of the article, by improving the S-function and the compact R-function method in the existing literature, a new model is given to reduce the computational burden of the Gaussian model by using the absolute value operation instead of the square operation, thus the calculation cost and the extraction cost are reduced. High operating efficiency. Finally, introduce a model based on this model In order to reduce the errors in the learning process and to analyze other optimization parameters, the linear sum of the transfer functions in the hybrid neural network is introduced. Nonlinear weighting and other measures. The brain has a fault recognition function and a self-healing mechanism controlled by the nervous system. To further reduce the brain's work. The rationale is to transfer the current research focus from the system analysis of the small world network to the network of millions of nodes, which will require the computer to have high processing performance the need for a large number of fast computing algorithms for sensor information processing has become a priority, not to say, The data is likely to be destroyed in the accident. In addition, a simple imitation of the human brain's neural network can demonstrate the rapid involvement of human experts Speed learning and accurate treatment of the problem of classification attributes. Although these tools are clearly unable to replace human experts, they are Problem diagnosis and decision support provide a basis. Self-adaptation should The genetic algorithm encoded by the variant particle swarm optimization (A MPSO) f (?) is used to update the base transfer function, which makes the modified function add The results from the basic model show that the CRBF model is better in each parameter (average number of iterations, convergence time, standard variance error and calculation time), and the optimal error is the CRBF model (0.0111) and the ZRBF (0. 0111) in the model trained by the PSO algorithm. The optimal error value of CRBF, SBF, ZRBF and GRBF is 0. 012923,0. 0, respectively. The best optimization error is 0. 009 and 0. 01109 in the nonlinear weighted mixed model of the inverse cosine by the PSO and the genetic algorithm, and the second is in production. In the linear mixed model, the optimal error is 0. 0103 and the nonlinear negative cosine weighting is obtained by using the PSO algorithm and the genetic algorithm. In the g-scale hybrid model, the optimal error is 0.011. As can be seen from the results, the PSO algorithm is proved to be an underground mine, a tunnel, Strong choice in road and other natural (such as landslides) rescue operations. The genetic algorithm model, especially the SBF, is well trained, but it is difficult and prone to error in rock penetration, but in the such as surface mining, hospital and The evacuation of buildings and so on is very effective. This article is divided into five chapters. Chapter 1 discusses complex The background, object, meaning of the adaptive system and the conceptual framework of the model are put forward. including an s function and a radial basis function, Methods: The third chapter makes a hypothesis about the route generation, and constructs the basic model. The fourth chapter discusses the proposed model. Several mixed cases, including linear and non-linear weighted hybrid models and their analysis, are presented in the fifth chapter for the proposed genetic algorithm. The analysis of the model in the particle swarm and the genetic algorithm is also discussed.
【学位授予单位】：江苏大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN929.5;TP212.9
本文编号：2320066
[Abstract]:Underground mining is one of the most dangerous work in the world. In the past two decades, there are major casualties and great loss of life and property in major accidents, such as fire, rockfall, flood, and toxic gas. The loss of life and the loss of property damage the stability and sustainable development of society. The various effective measures proposed by the Government, the mining industry, the engineers, scientists and computer experts are being mitigated. These include an underground mining model to predict safety and to take rescue measures in order to help track and rescue miners at risk through monitoring. However, most of the advanced computer models used to analyze evacuation problems and communication are typically operational When, for example, an additional square operation is included, a globally accepted Gaussian radial basis model is obtained, and as the network scale becomes large, there will be a need for a model that does not have a square operation Time. This paper focuses on the optimization of routing or transmission path (R). The accident site is constructed as a pure random event, and the information chain is estimated to be free from damage by establishing a neural network model. The probability is that, when the real accident happens, the robot can predict the information chain without damage immediately by using the neural network of the complex relation of memory and memory, Probability. If the prediction is positive, the robot waits to receive a rescue signal; otherwise it will go deeper into the formation and repeat this in that second part of the article, by improving the S-function and the compact R-function method in the existing literature, a new model is given to reduce the computational burden of the Gaussian model by using the absolute value operation instead of the square operation, thus the calculation cost and the extraction cost are reduced. High operating efficiency. Finally, introduce a model based on this model In order to reduce the errors in the learning process and to analyze other optimization parameters, the linear sum of the transfer functions in the hybrid neural network is introduced. Nonlinear weighting and other measures. The brain has a fault recognition function and a self-healing mechanism controlled by the nervous system. To further reduce the brain's work. The rationale is to transfer the current research focus from the system analysis of the small world network to the network of millions of nodes, which will require the computer to have high processing performance the need for a large number of fast computing algorithms for sensor information processing has become a priority, not to say, The data is likely to be destroyed in the accident. In addition, a simple imitation of the human brain's neural network can demonstrate the rapid involvement of human experts Speed learning and accurate treatment of the problem of classification attributes. Although these tools are clearly unable to replace human experts, they are Problem diagnosis and decision support provide a basis. Self-adaptation should The genetic algorithm encoded by the variant particle swarm optimization (A MPSO) f (?) is used to update the base transfer function, which makes the modified function add The results from the basic model show that the CRBF model is better in each parameter (average number of iterations, convergence time, standard variance error and calculation time), and the optimal error is the CRBF model (0.0111) and the ZRBF (0. 0111) in the model trained by the PSO algorithm. The optimal error value of CRBF, SBF, ZRBF and GRBF is 0. 012923,0. 0, respectively. The best optimization error is 0. 009 and 0. 01109 in the nonlinear weighted mixed model of the inverse cosine by the PSO and the genetic algorithm, and the second is in production. In the linear mixed model, the optimal error is 0. 0103 and the nonlinear negative cosine weighting is obtained by using the PSO algorithm and the genetic algorithm. In the g-scale hybrid model, the optimal error is 0.011. As can be seen from the results, the PSO algorithm is proved to be an underground mine, a tunnel, Strong choice in road and other natural (such as landslides) rescue operations. The genetic algorithm model, especially the SBF, is well trained, but it is difficult and prone to error in rock penetration, but in the such as surface mining, hospital and The evacuation of buildings and so on is very effective. This article is divided into five chapters. Chapter 1 discusses complex The background, object, meaning of the adaptive system and the conceptual framework of the model are put forward. including an s function and a radial basis function, Methods: The third chapter makes a hypothesis about the route generation, and constructs the basic model. The fourth chapter discusses the proposed model. Several mixed cases, including linear and non-linear weighted hybrid models and their analysis, are presented in the fifth chapter for the proposed genetic algorithm. The analysis of the model in the particle swarm and the genetic algorithm is also discussed.
【学位授予单位】：江苏大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN929.5;TP212.9
【参考文献】
相关期刊论文 前1条
1 彭程;潘玉民;;粒子群优化的RBF瓦斯涌出量预测[J];中国安全生产科学技术;2011年11期
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