POJ 1269 Intersecting Lines

 

Intersecting Lines
Time Limit: 1000MS Memory Limit: 10000K
Total Submissions: 4349 Accepted: 2096

Description

We all know that a pair of distinct points on a plane defines a line and that a pair of lines on a plane will intersect in one of three ways: 1) no intersection because they are parallel, 2) intersect in a line because they are on top of one another (i.e. they are the same line), 3) intersect in a point. In this problem you will use your algebraic knowledge to create a program that determines how and where two lines intersect.
Your program will repeatedly read in four points that define two lines in the x-y plane and determine how and where the lines intersect. All numbers required by this problem will be reasonable, say between -1000 and 1000.

Input

The first line contains an integer N between 1 and 10 describing how many pairs of lines are represented. The next N lines will each contain eight integers. These integers represent the coordinates of four points on the plane in the order x1y1x2y2x3y3x4y4. Thus each of these input lines represents two lines on the plane: the line through (x1,y1) and (x2,y2) and the line through (x3,y3) and (x4,y4). The point (x1,y1) is always distinct from (x2,y2). Likewise with (x3,y3) and (x4,y4).

Output

There should be N+2 lines of output. The first line of output should read INTERSECTING LINES OUTPUT. There will then be one line of output for each pair of planar lines represented by a line of input, describing how the lines intersect: none, line, or point. If the intersection is a point then your program should output the x and y coordinates of the point, correct to two decimal places. The final line of output should read "END OF OUTPUT".

Sample Input

5
0 0 4 4 0 4 4 0
5 0 7 6 1 0 2 3
5 0 7 6 3 -6 4 -3
2 0 2 27 1 5 18 5
0 3 4 0 1 2 2 5

Sample Output

INTERSECTING LINES OUTPUT
POINT 2.00 2.00
NONE
LINE
POINT 2.00 5.00
POINT 1.07 2.20
END OF OUTPUT
 
 

 

内容概要:本文介绍了一种利用元启发式算法(如粒子群优化,PSO)优化线性二次调节器(LQR)控制器加权矩阵的方法,专门针对复杂的四级倒立摆系统。传统的LQR控制器设计中,加权矩阵Q的选择往往依赖于经验和试错,而这种方法难以应对高维度非线性系统的复杂性。文中详细描述了如何将控制器参数优化问题转化为多维空间搜索问题,并通过MATLAB代码展示了具体实施步骤。关键点包括:构建非线性系统的动力学模型、设计适应度函数、采用对数缩放技术避免局部最优、以及通过实验验证优化效果。结果显示,相比传统方法,PSO优化后的LQR控制器不仅提高了稳定性,还显著减少了最大控制力,同时缩短了稳定时间。 适合人群:控制系统研究人员、自动化工程专业学生、从事机器人控制或高级控制算法开发的技术人员。 使用场景及目标:适用于需要精确控制高度动态和不确定性的机械系统,特别是在处理多自由度、强耦合特性的情况下。目标是通过引入智能化的参数寻优手段,改善现有控制策略的效果,降低人为干预的需求,提高系统的鲁棒性和性能。 其他说明:文章强调了在实际应用中应注意的问题,如避免过拟合、考虑硬件限制等,并提出了未来研究方向,例如探索非对角Q矩阵的可能性。此外,还分享了一些实践经验,如如何处理高频抖动现象,以及如何结合不同类型的元启发式算法以获得更好的优化结果。
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