C++ OOP Design

本篇博客深入探讨了面向对象编程中继承与组合的区别及应用,解释了如何在设计模式中灵活使用这两种机制来增强代码的可扩展性和复用性。通过对比实例,清晰展示了它们各自的特点及适用场景。
Extensibility
With play( )defined as virtual in the base class, you can add as 
many new types as you want without changing the tune( )
function. In a well-designed OOP program, most or all of your 
functions will follow the model of tune( )and communicate only 
with the base-class interface.
Such a program is extensible because 
you can add new functionality by inheriting new data types from 
the common base class. The functions that manipulate the base class interface will not need to be changed at all to accommodate 

the new classes. 



You’ve seen in this chapter that it’s impossible to understand, or 
even create, an example of polymorphism without using data 
abstraction and inheritance. Polymorphism is a feature that cannot 
be viewed in isolation (like constor a switchstatement, for 
example), but instead works only in concert, as part of a “big 
picture” of class relationships. People are often confused by other, 
non-object-oriented features of C++, like overloading and default 
arguments, which are sometimes presented as object-oriented. 
Don’t be fooled; if it isn’t late binding, it isn’t polymorphism.
 



Choosing composition vs. inheritance
Both composition and inheritance place subobjects inside your new 
class. Both use the constructor initializer list to construct these 
subobjects. You may now be wondering what the difference is 
between the two, and when to choose one over the other. 
Composition is generally used when you want the features of an 
existing class inside your new class, but not its interface. That is, 
you embed an object to implement features of your new class, but 
the user of your new class sees the interface you’ve defined rather 
than the interface from the original class. To do this, you follow the 
typical path of embedding private objects of existing classes inside 
your new class. 
Occasionally, however, it makes sense to allow the class user to 
directly access the composition of your new class, that is, to make 
the member objects public. The member objects use access control 
themselves, so this is a safe thing to do and when the user knows 
you’re assembling a bunch of parts, it makes the interface easier to 
understand.


From<thinking in C++>


提供了基于BP(Back Propagation)神经网络结合PID(比例-积分-微分)控制策略的Simulink仿真模型。该模型旨在实现对杨艺所著论文《基于S函数的BP神经网络PID控制器及Simulink仿真》中的理论进行实践验证。在Matlab 2016b环境下开发,经过测试,确保能够正常运行,适合学习和研究神经网络在控制系统中的应用。 特点 集成BP神经网络:模型中集成了BP神经网络用于提升PID控制器的性能,使之能更好地适应复杂控制环境。 PID控制优化:利用神经网络的自学习能力,对传统的PID控制算法进行了智能调整,提高控制精度和稳定性。 S函数应用:展示了如何在Simulink中通过S函数嵌入MATLAB代码,实现BP神经网络的定制化逻辑。 兼容性说明:虽然开发于Matlab 2016b,但理论上兼容后续版本,可能会需要调整少量配置以适配不同版本的Matlab。 使用指南 环境要求:确保你的电脑上安装有Matlab 2016b或更高版本。 模型加载: 下载本仓库到本地。 在Matlab中打开.slx文件。 运行仿真: 调整模型参数前,请先熟悉各模块功能和输入输出设置。 运行整个模型,观察控制效果。 参数调整: 用户可以自由调节神经网络的层数、节点数以及PID控制器的参数,探索不同的控制性能。 学习和修改: 通过阅读模型中的注释和查阅相关文献,加深对BP神经网络与PID控制结合的理解。 如需修改S函数内的MATLAB代码,建议有一定的MATLAB编程基础。
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