为探寻双滚筒式采煤机在不同工况下各滑靴受力的变化规律，分析整机受力状态与数学模型结构的关系，改善采煤机整机受力状态，建立采煤机整机受力的数学模型，并预先给出模型状态判断，基于最小二乘原理和广义逆，利用 matlab 编制计算程序，解算出不同工况下各滑靴受力的最佳逼近值，得到了煤层倾角和仰（俯）角变化对滑靴受力变化的影响曲线.结果表明：所建模型可以反映生产实际中各滑靴受力情况，利用最小二乘原理解算采煤机不相容奇异力学模型的方法有效，得到外负载与各滑靴受力的平衡变化规律.

Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems. With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

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