English—Spoken English

本文分享了作者通过每日练习英语口语并参与英语活动来提高口语能力的经验。内容包括使用《365天英语口语》和《900句美国英语》等资源进行自我学习的过程,以及在英语环境中沟通的重要性。此外,还提到了通过情景表演和游戏来增加学习乐趣的方法。

Spoken Enlish

  I havestudy spoken english a weak since spring festival.

Spoken english,Spoen english, it must need more speak ,and now we speak english everyday's everytime.

It is veryuesful and impotant with our spoken english that let ourselves in the english environment.

All english environment alse help us short brain our chinese think.


My Study 

  These days,I have studied the 365 spoken english and the 900 sentence of american english.we have ourselves name is hungry fill(Sorry,I don’t know the real english name).These video fill our spoken english,but the most important is communication.The day's communication is english,it must done by everybody.Ifhave one peason doesn't do it ,the english environment will be broken.It affect everybody and not only youself.


My Problem Of Spoken

  I found some questions with me these days.I english communicaiton time is short,and my english words is short.I can speak the english words when I want to express mymeans.So advance my qunatity of english listen is important.The english comunication also help me find my short english words,and to seachit,finally,advance my english.

 

English Show

  I was vary happy with english activity in the yesterday evening.we play a scene show,Ithink it's terrific,though we have a little mistake.we make a sound with the HongEn English(501th),the began song is very good,and we almost follew the next video.we played a killer game with another team.The game is terrific and everybody is very happy form their smile.


Summary

  Advance my enlish communication.Found my short,and fill it.Speak english more and more.

  Write the english blog as my first english blog and keep it.

MATLAB代码实现了一个基于多种智能优化算法优化RBF神经网络的回归预测模型,其核心是通过智能优化算法自动寻找最优的RBF扩展参数(spread),以提升预测精度。 1.主要功能 多算法优化RBF网络:使用多种智能优化算法优化RBF神经网络的核心参数spread。 回归预测:对输入特征进行回归预测,适用于连续值输出问题。 性能对比:对比不同优化算法在训练集和测试集上的预测性能,绘制适应度曲线、预测对比图、误差指标柱状图等。 2.算法步骤 数据准备:导入数据,随机打乱,划分训练集和测试集(默认7:3)。 数据归一化:使用mapminmax将输入和输出归一化到[0,1]区间。 标准RBF建模:使用固定spread=100建立基准RBF模型。 智能优化循环: 调用优化算法(从指定文件夹中读取算法文件)优化spread参数。 使用优化后的spread重新训练RBF网络。 评估预测结果,保存性能指标。 结果可视化: 绘制适应度曲线、训练集/测试集预测对比图。 绘制误差指标(MAE、RMSE、MAPE、MBE)柱状图。 十种智能优化算法分别是: GWO:灰狼算法 HBA:蜜獾算法 IAO:改进天鹰优化算法,改进①:Tent混沌映射种群初始化,改进②:自适应权重 MFO:飞蛾扑火算法 MPA:海洋捕食者算法 NGO:北方苍鹰算法 OOA:鱼鹰优化算法 RTH:红尾鹰算法 WOA:鲸鱼算法 ZOA:斑马算法
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