它们都依据IEEE 754标准，该标准为32位浮点和64位双精度浮点二进制小数定义了二进制标准。
These are based on the IEEE 754 standard, which defines a binary standard for 32-bit floating point and 64-bit double precision floating point binary-decimal Numbers.
浮点是一个非常类似的概念，除了计算机使用二进制而不是十进制作为基础。
Floating point is a very similar concept, except that computers use binary rather than decimal as their base.
EXI引入了数据类型，比如二进制、布尔值、小数、浮点数、整数、无符号整数，以及日期-时间。
EXI introduces data types such as Binary, Boolean, Decimal, Float, Integer, Unsigned Integer, and Date-Time.
比如0.1不能被表示为二进制数，所以被四舍五入储存为双精度浮点数。
For example, 0.1 cannot be represented binary, and therefore is rounded when stored in a double. Let me show you using python.
浮点数和双精度数字的二进制表示。
因为在内部，计算机使用一种非精确的(二进制浮点数)形式表示诸如0.1,0.2,0.3之类的小数。
Because internally, computers use a format (binary floating-point) that cannot accurately represent a number like 0.1, 0.2 or 0.3 at all.
二进制浮点点类型是更快的工作比小数。
Floating binary point types are much faster to work with than decimals.
浮点值通常是在二进制格式存储在计算机内存中。
Floating-point value are typically stored in computer memory in binary format.
编辑浮点值时，由于要将小数部分从十进制转换为二进制，因此所得的结果可能存在微小误差。
Editing floating-point values can result in minor inaccuracies because of decimal-to-binary conversion of fractional components.
二进制浮点表示的转换十进制表示可以损耗，根据您的转换设置。
The conversion from binary floating-point representation to decimal representation can be lossy, depending on your conversion Settings.
对采用二进制编码和浮点数编码，遗传算法的执行效率进行了研究，并结合实例，说明浮点数编码在多参数寻优中具有很好的效率。
Research on the efficiency of binary encode and floating encode is demonstrated here. And by actual sample, the high efficiency of floating encode genetic algorithms is shown.
对采用二进制编码和浮点数编码，遗传算法的执行效率进行了研究，并结合实例，说明浮点数编码在多参数寻优中具有很好的效率。
Research on the efficiency of binary encode and floating encode is demonstrated here. And by actual sample, the high efficiency of floating encode genetic algorithms is shown.
应用推荐