针对多输入-多输出(MIMO)非线性系统基于模糊基函数向量提出了一种新的自适应控制方法。
In this paper, a novel adaptive control approach based on fuzzy basis function vector is presented for Multi input and Multi output (MIMO) nonlinear systems.
但是，其中一个把函数映到向量。
我这里有一个函数的等值线图，还有一个蓝色的向量。
So, here I have a contour plot of a function, and I have a blue vector.
因为MASS向量函数要比对一个标准库函数的重复访问快很多(倍数接近30倍)，所以最后得到的性能改善效果将会是惊人的。
Since the MASS vector functions are much faster (by a factor of up to about 30) than a repeated call to a standard library function, the resulting performance improvement can be substantial.
梯度向量方向是,在给定点处指向函数增加得最快的方向。
The gradient is the direction in which the function increases the most quickly at that point.
如果汇编器不能对程序进行向量化，它会自动试着调用等价MASS标量函数。
If the program is such that the compiler cannot vectorize, it automatically tries to call the equivalent MASS scalar functions.
也就是向量场f实际上是一个函数的梯度。
Say that f, our vector field is actually the gradient of some function.
这只是一个由向量场得到的,有点特别的函数，但这跟别的函数是一样的。
This is just a particular kind of function that you get out of a vector field, but it is like any function.
我们说，当一个二元函数存在时，就有梯度向量。
So, we said when we have a function of two variables, we have the gradient vector.
空间中的向量场的旋度，是一个向量场，而不是一个标量函数，我必须告诉你们。
The curl of a vector field in space is actually a vector field, not a scalar function. I have delayed the inevitable.
实际上，找出一个函数，其梯度是这个向量场并不难。
Well, actually here it is not very hard to find a function whose gradient is this vector field.
但它们不一定是，某个以该向量场为梯度的函数的等值线。
But they won't be the level curves of a function for which this is the gradient.
对这个函数取梯度，你应该又得到这个向量场。
If you take the gradient of this, you should get again this vector field over there.
如果说函数的梯度是向量，那么向量场的散度就是函数。
So, if the gradient of a function is a vector, the divergence of a vector field is a function.
嵌入的ts()函数通过向量glarp$livingroom创建一个时间序列对象。
The embedded ts() function creates a time series object out of the vector glarp$livingroom.
除了一系列的选项之外，当 -qipa选项处于可用状态时，如果汇编器不能进行向量化，那么它会试着在决定调用它们之前去内联MASS标量函数。
In addition to any of the preceding sets of options, when the -qipa option is in effect, if the compiler cannot vectorize, it tries to inline the MASS scalar functions before deciding to call them.
幸运的是，PHP提供了ip2long函数，它提供了同样的功能，而且不依赖于数据库向量。
Fortunately PHP provides the ip2long function that offers the same functionality and is not dependent on the database vendor.
对于自动化的标量或者向量，汇编器会使用汇编器库libxlopt . a中包含的mass函数的版本。
For automatic vectorization or scalarization, the compiler USES versions of the MASS functions contained in the compiler library libxlopt.a.
最后，我决定使用vec函数—它的速度相当快，对于处理dna而言，它无疑是正确的选择(本质上，DNA是个位向量，一个内建的Perl数据结构)。
Finally, I settled on the vec function — it is quite fast, and was the right choice for handling DNA (essentially, the DNA is a bit vector, a built-in Perl data structure).
要看向量场,能否写成，其中f是同一个函数。
We want to know whether a given vector field with components P, Q and R can be written as f sub x, f sub y and f sub z for a same function f.
函数matrix 、array和dim是用于设置向量的维的简单函数。
The functions matrix , array , and dim are simply convenience functions for setting the dimensions of a vector.
也就是，如果我给你一个单位向量，试问，我沿着这个方向移动，我的函数值会变化得多快呢？
OK, so if I give you a unit vector, you can ask yourself, if I move in the direction, how quickly will my function change?
我想找出这个向量场的势函数。
那就是梯度，另一个把向量映到函数。
That's gradient. The other one goes from vectors to functions.
也就是说，如果朝该方向运动，函数值变动得最剧烈，那么相应的斜率就是梯度向量的模长。
OK, so if I go in that direction, which gives me the fastest increase, then the corresponding slope will be the length of the gradient.
如果一个向量场不可能是梯度场，那我们就不应该尝试去找势函数。
If we have a vector field that cannot possibly be a gradient then we shouldn't try to look for a potential.
在诊断模型中，应用APEX网络提取分类信息，压缩向量空间维数，利用前馈网络建立其类型识别函数。
In the model, using APEX network extracts classification information and condenses vector space dimensions, making use of feedforward network establishes the classification recognition function.
这是第二张函数图,我在上面添加了函数xy的轮廓，放在了向量场上面。
So, here's version two of my plot where I've added the contour plot of a function, xy on top of the vector field.
目前，支持向量机在模式识别、函数逼近、数据挖掘和文本自动分类中均有很好的应用。
Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization.
目前，支持向量机在模式识别、函数逼近、数据挖掘和文本自动分类中均有很好的应用。
Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization.
应用推荐