young for you

一首充满诗意的歌曲描绘了在加州的浪漫故事,歌词中穿插着驾车、海滩等元素,展现了作者对爱情的向往与追求。
young for you
sunday's coming i wanna drive my car
to your apartment with present like a star
forecaster said the weather may be rainy hard
but i know the sun will shine for us

oh lazy seagull fly me from the dark
i dress my jeans and feed my monkey banana
then i think my age how old,skyline how far
or we need each other in california

*you show me your body before night comes down
i touch your face and promise to stay ever young
on this ivory beach we kissed so long
it seems that the passion's never gone

*you sing me your melody and i feel so please
i want you to want me to keep your dream
together we'll run wild by a summer symphony
this is what we enjoyed not a fantasy

the tin-man's surfing i wanna try my luck
to the top of tide rip like just have some drugs
i know you have no blame for my proud moonish heart
welcome to the golden beatnik park

oh diamond seashore drag me from the yard
incredible sunward i watch as you're in photograph
for camera your smile's so sweet,palm trees' so lush
would you believe my honey it's califonia


kaleidoscope


i look upon my life as a trip
to try to see to enjoy
i look upon your heart as pic.
its color's never in faintness

you're wayward to do in load
no remorse to show
life's just a kaleidoscope
that is what you've got

*some days' already passed away
there are left memory
i can't reach it forever
for it doesn't once more

bridge:
let me down in deep hue
playin' acid with you
we needn't to pretend
it's alright be naked between u and me

but i've lost you baby in yesterday
it has broken my dream
how the past flame be the same
you are my one and only

*yesterday's just passed away
something goes clearly
i can't touch it forever
for it's too far away

*some days' already passed away
there are left memory
i can't reach it forever
for it doesn't once more

ah*****ah*****
ah*****ah*****
(Kriging_NSGA2)克里金模型结合多目标遗传算法求最优因变量及对应的最佳自变量组合研究(Matlab代码实现)内容概要:本文介绍了克里金模型(Kriging)与多目标遗传算法NSGA-II相结合的方法,用于求解最优因变量及其对应的最佳自变量组合,并提供了完整的Matlab代码实现。该方法首先利用克里金模型构建高精度的代理模型,逼近复杂的非线性系统响应,减少计算成本;随后结合NSGA-II算法进行多目标优化,搜索帕累托前沿解集,从而获得多个最优折衷方案。文中详细阐述了代理模型构建、算法集成流程及参数设置,适用于工程设计、参数反演等复杂优化问题。此外,文档还展示了该方法在SCI一区论文中的复现应用,体现了其科学性与实用性。; 适合人群:具备一定Matlab编程基础,熟悉优化算法和数值建模的研究生、科研人员及工程技术人员,尤其适合从事仿真优化、实验设计、代理模型研究的相关领域工作者。; 使用场景及目标:①解决高计算成本的多目标优化问题,通过代理模型降低仿真次数;②在无法解析求导或函数高度非线性的情况下寻找最优变量组合;③复现SCI高水平论文中的优化方法,提升科研可信度与效率;④应用于工程设计、能源系统调度、智能制造等需参数优化的实际场景。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现过程,重点关注克里金模型的构建步骤与NSGA-II的集成方式,建议自行调整测试函数或实际案例验证算法性能,并配合YALMIP等工具包扩展优化求解能力。
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