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本文介绍了运行时刻(RunTime)和编译时刻(CompileTime)。编译时刻是将源代码编译成机器码的过程,而运行时刻是指编译后的程序被用户打开运行的阶段。同时还提到这两个术语常被用于指代不同类型的错误,如编译时错误和运行时错误。
关于 RunTime(运行时刻) 和 CompileTime(编译时刻)

Runtime and compile time are programming terms that refer to different stages of software program development. In order to create a program, a developer first writes source code, which defines how the program will function. Small programs may only contain a few hundred lines of source code, while large programs may contain hundreds of thousands of lines of source code. The source code must be compiled into machine code in order to become and executable program. This compilation process is referred to as compile time.
A compiled program can be opened and run by a user. When an application is running, it is called runtime.
The terms “runtime” and “compile time” are often used by programmers to refer to different types of errors. A compile time error is a problem such as a syntax error or missing file reference that prevents the program from successfully compiling. The compiler produces compile time errors and usually indicates what line of the source code is causing the problem.
If a program’s source code has already been compiled into an executable program, it may still have bugs that occur while the program is running. Examples include features that don’t work, unexpected program behavior, or program crashes. These types of problems are called runtime errors since they occur at runtime.

内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等多种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高校研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
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