基于粒子群优化算法.doc
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基于粒子群优化算法,摘要近年来，随着海上船舶数量的增加和船舶吨位的增大，船舶航行安全问题日益重要，如何保证海上船舶的航行安全是一个迫切需要解决的问题，同时也是许多专家和学者研究的重点和热点。合理的船舶避碰方案的确定和船舶碰撞危险度的确定是保证海上船舶航行安全的重要问题，本文首先通过粒子群优化算法及改进的两种粒子群优化算法来进行船舶避碰方案...
内容介绍
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摘 要
近年来，随着海上船舶数量的增加和船舶吨位的增大，船舶航行安全问题日益重要，如何保证海上船舶的航行安全是一个迫切需要解决的问题，同时也是许多专家和学者研究的重点和热点。
合理的船舶避碰方案的确定和船舶碰撞危险度的确定是保证海上船舶航行安全的重要问题，本文首先通过粒子群优化算法及改进的两种粒子群优化算法来进行船舶避碰方案的确定。然后考虑到船舶碰撞危险度的确定是一个很复杂的过程，受很多因素的影响，具有很强的非线性特征，本文基于粒子群算法和神经网络的特点，构建了粒子群神经网络模型，并通过函数拟合、分类和广义异或问题进行验证，最后将粒子群神经网络模型应用到船舶碰撞危险度的确定。
论文的主要研究成果可归纳如下：
(1) 将粒子群优化算法、改进的混沌粒子群优化算法和免疫粒子群优化算法三种算法应用到船舶避碰方案的确定上，通过本船分别与一个目标船、两个目标船和三个目标船形成的各种会遇态势进行仿真，并与穷举法的结果相比较，证明了这三种算法可以取得比较好的效果，可以应用到船舶避碰方案的确定上。
(2) 构建了粒子群神经网络模型。针对神经网络结构的不确定性和权阈值的随机性，本文首先通过混合粒子群优化算法来同时确定神经网络的网络结构和权阈值，然后再通过BP算法进行训练。主要表现在通过二进制粒子群算法确定各个隐含层的神经元数和十进制粒子群优化算法确定网络的权阈值。
(3) 通过函数拟合数值实验、Iris花分类、Wine数据集分类、LED分类和广义异或问题，来验证粒子群神经网络模型的性能，结果表明粒子群神经网络模型可以取得较好的效果。
(4) 将粒子群神经网络模型应用到船舶碰撞危险度的确定上。分别通过具有影响碰撞危险度的两个因素和六个因素的样本数据进行碰撞危险度的确定，取得了较好的效果。
关键词 粒子群优化算法；神经网络；避碰幅度；船舶碰撞危险度
Abstract
In recent years, with the number of ships increasing and ships’ weights growing, navigation security issues are increasingly important. How to ensure the safety of ships sailing is an urgent need to resolve. At the same time, many experts and scholars are studying the emphasis.
Reasonable ways to ships collision avoidance and the suitable determines of ships collision avoidance are the guarantee of the safety navigation. Firstly, this thesis gives the ways of ships collision avoidance by particle swarm optimization (PSO) algorithm and other two improved PSO algorithm. Then considering the determination of ships collision risk is a very complex process, and it is affected by many factors. Also, it has a strong feature of the nonlinear. Based on the features of PSO algorithm and the neural network, this thesis constructs the model of neutral network. It is verified through function fitting, classification and general XOR problem. At last, this model is applied to determination of ships collision avoidance.
The main research results in this thesis can be summarized as follows:
(1) The ways of ships collision avoidance are, respectively, determined by PSO algorithm, improved chaotic PSO algorithm and improved immunity PSO algorithm, and the simulation is done with a target ship, two and three goals in various encounter posture. In comparison with the exhaustive law, it is improved that the three algorithms can achieve better results, and it can be applied to determination of ways of ships collision avoidance.
(2) The model of neural network based on PSO algorithm is constructed. Considering the random of the number of the hidden layers and the determination of weight for BP neural network, firstly, this model utilizes hybrid particle swarm optimization to optimize the structure and initial weights for the neural network, and then training by BP. Its performance is mainly through the binary PSO algorithm to determine the threshold of the neutral network and decimal PSO algorithm to determine the neurons number of the hidden layers.
(3) Function fitting, Iris classification, Wine classification, LED classification and general XOR problem are used to verify the performance of neural network based on PSO algorithm. The computing results show that the model can achieve better results.
(4) Determines the ships collision risk through the model of neural network based on PSO algorithm. Simulated by the sample data of two actors and six actors respectively, the results show that the model can achieve better results.
Key Words PSO algorithm; neural network; collision avoidance amplitude; ships collision risk
目 录
摘 要 I
Abstract III
第1章 绪论 1
1.1 课题的研究背景和现状 1
1.1.1 船舶避碰的研究背景和现状 1
1.1.2 选题的背景 3
1.2 课题研究内容及主要成果 5
1.3 本文的章节安排和结构 5
第2章 船舶避碰基础知识 7
2.1 船舶避碰阶段的划分 7
2.2 船舶避碰过程 8
2.3 船舶会遇局面划分 10
2.4 安全会遇距离的确定 11
2.5 避碰行动方式 12
2.6 避让行动时机和幅度 12
2.7 复航 14
2.8 多船会遇 15
2.9 本章小结 15
第3章 基于粒子群优化算法的船舶避碰决策 17
3.1 粒子群优化算法 17
3.1.1 基本粒子群优化算法原理 17
3.1.2 基本粒子群优化算法描述 17
3.1.3 基本粒子群算法参数的设置 18
3.2 混沌粒子群优化算法 18
3.3 免疫粒子群优化算法 21
3.4 船舶避碰方案的确定 23
3.4.1 船舶..
近年来，随着海上船舶数量的增加和船舶吨位的增大，船舶航行安全问题日益重要，如何保证海上船舶的航行安全是一个迫切需要解决的问题，同时也是许多专家和学者研究的重点和热点。
合理的船舶避碰方案的确定和船舶碰撞危险度的确定是保证海上船舶航行安全的重要问题，本文首先通过粒子群优化算法及改进的两种粒子群优化算法来进行船舶避碰方案的确定。然后考虑到船舶碰撞危险度的确定是一个很复杂的过程，受很多因素的影响，具有很强的非线性特征，本文基于粒子群算法和神经网络的特点，构建了粒子群神经网络模型，并通过函数拟合、分类和广义异或问题进行验证，最后将粒子群神经网络模型应用到船舶碰撞危险度的确定。
论文的主要研究成果可归纳如下：
(1) 将粒子群优化算法、改进的混沌粒子群优化算法和免疫粒子群优化算法三种算法应用到船舶避碰方案的确定上，通过本船分别与一个目标船、两个目标船和三个目标船形成的各种会遇态势进行仿真，并与穷举法的结果相比较，证明了这三种算法可以取得比较好的效果，可以应用到船舶避碰方案的确定上。
(2) 构建了粒子群神经网络模型。针对神经网络结构的不确定性和权阈值的随机性，本文首先通过混合粒子群优化算法来同时确定神经网络的网络结构和权阈值，然后再通过BP算法进行训练。主要表现在通过二进制粒子群算法确定各个隐含层的神经元数和十进制粒子群优化算法确定网络的权阈值。
(3) 通过函数拟合数值实验、Iris花分类、Wine数据集分类、LED分类和广义异或问题，来验证粒子群神经网络模型的性能，结果表明粒子群神经网络模型可以取得较好的效果。
(4) 将粒子群神经网络模型应用到船舶碰撞危险度的确定上。分别通过具有影响碰撞危险度的两个因素和六个因素的样本数据进行碰撞危险度的确定，取得了较好的效果。
关键词 粒子群优化算法；神经网络；避碰幅度；船舶碰撞危险度
Abstract
In recent years, with the number of ships increasing and ships’ weights growing, navigation security issues are increasingly important. How to ensure the safety of ships sailing is an urgent need to resolve. At the same time, many experts and scholars are studying the emphasis.
Reasonable ways to ships collision avoidance and the suitable determines of ships collision avoidance are the guarantee of the safety navigation. Firstly, this thesis gives the ways of ships collision avoidance by particle swarm optimization (PSO) algorithm and other two improved PSO algorithm. Then considering the determination of ships collision risk is a very complex process, and it is affected by many factors. Also, it has a strong feature of the nonlinear. Based on the features of PSO algorithm and the neural network, this thesis constructs the model of neutral network. It is verified through function fitting, classification and general XOR problem. At last, this model is applied to determination of ships collision avoidance.
The main research results in this thesis can be summarized as follows:
(1) The ways of ships collision avoidance are, respectively, determined by PSO algorithm, improved chaotic PSO algorithm and improved immunity PSO algorithm, and the simulation is done with a target ship, two and three goals in various encounter posture. In comparison with the exhaustive law, it is improved that the three algorithms can achieve better results, and it can be applied to determination of ways of ships collision avoidance.
(2) The model of neural network based on PSO algorithm is constructed. Considering the random of the number of the hidden layers and the determination of weight for BP neural network, firstly, this model utilizes hybrid particle swarm optimization to optimize the structure and initial weights for the neural network, and then training by BP. Its performance is mainly through the binary PSO algorithm to determine the threshold of the neutral network and decimal PSO algorithm to determine the neurons number of the hidden layers.
(3) Function fitting, Iris classification, Wine classification, LED classification and general XOR problem are used to verify the performance of neural network based on PSO algorithm. The computing results show that the model can achieve better results.
(4) Determines the ships collision risk through the model of neural network based on PSO algorithm. Simulated by the sample data of two actors and six actors respectively, the results show that the model can achieve better results.
Key Words PSO algorithm; neural network; collision avoidance amplitude; ships collision risk
目 录
摘 要 I
Abstract III
第1章 绪论 1
1.1 课题的研究背景和现状 1
1.1.1 船舶避碰的研究背景和现状 1
1.1.2 选题的背景 3
1.2 课题研究内容及主要成果 5
1.3 本文的章节安排和结构 5
第2章 船舶避碰基础知识 7
2.1 船舶避碰阶段的划分 7
2.2 船舶避碰过程 8
2.3 船舶会遇局面划分 10
2.4 安全会遇距离的确定 11
2.5 避碰行动方式 12
2.6 避让行动时机和幅度 12
2.7 复航 14
2.8 多船会遇 15
2.9 本章小结 15
第3章 基于粒子群优化算法的船舶避碰决策 17
3.1 粒子群优化算法 17
3.1.1 基本粒子群优化算法原理 17
3.1.2 基本粒子群优化算法描述 17
3.1.3 基本粒子群算法参数的设置 18
3.2 混沌粒子群优化算法 18
3.3 免疫粒子群优化算法 21
3.4 船舶避碰方案的确定 23
3.4.1 船舶..
