悬浮层

悬浮窗交互
本文介绍了一个使用JavaScript实现的页面悬浮窗口,该窗口可以根据用户的滚动位置进行动态调整,并支持鼠标拖动定位。通过监听滚动事件和鼠标事件,实现了悬浮窗口跟随滚动条移动及手动拖拽的功能。
<divid=sohususpend
style="LEFT:90%;POSITION:absolute;TOP:50%;VISIBILITY:visible;width:100px;height:74px"><ahref="#11"><imgsrc="/image/png-097.gif"width="100"height="100"border="0"></a></div>
<p><scriptlanguage=JavaScript>...
self.onError
=null;
currentX
=currentY=0;
whichIt
=null;
lastScrollX
=0;lastScrollY=0;
NS
=(document.layers)?1:0;
IE
=(document.all)?1:0;
functionheartBeat()...{
if(IE)...{diffY=document.body.scrollTop;diffX=document.body.scrollLeft;}
if(NS)...{diffY=self.pageYOffset;diffX=self.pageXOffset;}
if(diffY!=lastScrollY)...{
percent
=.1*(diffY-lastScrollY);
if(percent>0)percent=Math.ceil(percent);
elsepercent=Math.floor(percent);
if(IE)document.all.sohususpend.style.pixelTop+=percent;
if(NS)document.sohususpend.top+=percent;
lastScrollY
=lastScrollY+percent;
}

if(diffX!=lastScrollX)...{
percent
=.1*(diffX-lastScrollX);
if(percent>0)percent=Math.ceil(percent);
elsepercent=Math.floor(percent);
if(IE)document.all.sohususpend.style.pixelLeft+=percent;
if(NS)document.sohususpend.left+=percent;
lastScrollX
=lastScrollX+percent;
}

}

functioncheckFocus(x,y)...{
stalkerx
=document.sohususpend.pageX;
stalkery
=document.sohususpend.pageY;
stalkerwidth
=document.sohususpend.clip.width;
stalkerheight
=document.sohususpend.clip.height;
if((x>stalkerx&&x<(stalkerx+stalkerwidth))&&(y>stalkery&&y<(stalkery+stalkerheight)))returntrue;
elsereturnfalse;
}

functiongrabIt(e)...{
if(IE)...{
whichIt
=event.srcElement;
while(whichIt.id.indexOf("sohususpend")==-1)...{
whichIt
=whichIt.parentElement;
if(whichIt==null)...{returntrue;}
}

whichIt.style.pixelLeft
=whichIt.offsetLeft;
whichIt.style.pixelTop
=whichIt.offsetTop;
currentX
=(event.clientX+document.body.scrollLeft);
currentY
=(event.clientY+document.body.scrollTop);
}
else...{
window.captureEvents(Event.MOUSEDOWN);
if(checkFocus(e.pageX,e.pageY))...{
whichIt
=document.sohususpend;
StalkerTouchedX
=e.pageX-document.sohususpend.pageX;
StalkerTouchedY
=e.pageY-document.sohususpend.pageY;
}

}

returntrue;
}

functionmoveIt(e)...{
if(whichIt==null)...{returnfalse;}
if(IE)...{
newX
=(event.clientX+document.body.scrollLeft);
newY
=(event.clientY+document.body.scrollTop);
distanceX
=(newX-currentX);distanceY=(newY-currentY);
currentX
=newX;currentY=newY;
whichIt.style.pixelLeft
+=distanceX;
whichIt.style.pixelTop
+=distanceY;
if(whichIt.style.pixelTop<document.body.scrollTop)whichIt.style.pixelTop=document.body.scrollTop;
if(whichIt.style.pixelLeft<document.body.scrollLeft)whichIt.style.pixelLeft=document.body.scrollLeft;
if(whichIt.style.pixelLeft>document.body.offsetWidth-document.body.scrollLeft-whichIt.style.pixelWidth-20)whichIt.style.pixelLeft=document.body.offsetWidth-whichIt.style.pixelWidth-20;
if(whichIt.style.pixelTop>document.body.offsetHeight+document.body.scrollTop-whichIt.style.pixelHeight-5)whichIt.style.pixelTop=document.body.offsetHeight+document.body.scrollTop-whichIt.style.pixelHeight-5;
event.returnValue
=false;
}
else...{
whichIt.moveTo(e.pageX
-StalkerTouchedX,e.pageY-StalkerTouchedY);
if(whichIt.left<0+self.pageXOffset)whichIt.left=0+self.pageXOffset;
if(whichIt.top<0+self.pageYOffset)whichIt.top=0+self.pageYOffset;
if((whichIt.left+whichIt.clip.width)>=(window.innerWidth+self.pageXOffset-17))whichIt.left=((window.innerWidth+self.pageXOffset)-whichIt.clip.width)-17;
if((whichIt.top+whichIt.clip.height)>=(window.innerHeight+self.pageYOffset-17))whichIt.top=((window.innerHeight+self.pageYOffset)-whichIt.clip.height)-17;
returnfalse;
}

returnfalse;
}

functiondropIt()...{
whichIt
=null;
if(NS)window.releaseEvents(Event.MOUSEDOWN);
returntrue;
}

if(NS)...{
window.captureEvents(Event.MOUSEUP
|Event.MOUSEDOWN);
window.onmousedown
=grabIt;
window.onmousemove
=moveIt;
window.onmouseup
=dropIt;
}

if(IE)...{
document.onmousedown
=grabIt;
document.onmousemove
=moveIt;
document.onmouseup
=dropIt;
}

if(NS||IE)action=window.setInterval("heartBeat()",1);
</script>
基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
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