記得去上第一節課的時候，老師就問，你們知道什么是自適應嗎？還舉了個例子說，北方人來到了南方，
會覺得天氣特別熱，飲食也不太一樣，這時他就開始去慢慢適應南方的環境，自適應就開始了。
The adaptive linear combiner, which is the simplest and most widely applicable adaptive processor.
Several ways to classify adaptive schemes have been proposed in the literature. It is most convenient
here to begin by thinking in terms of open-loop and closed-loop adaptation. The open-loop adaptive process
involves making measurements of input or environmental characteristics, applying this information to a formula
or to a computational algorithm, and using the results to set the adjustments of the adaptive system. Closed-loop
adaptation, on the other hand, involves automatic experimentation with these adjustments and knowledge of their
outcome in order to optimize a measures system performance. The latter process may be called adaptation by
"performance feedback".
In the adaptation process with performance feedback. the weight vector of the linear combiner is adjusted to cause
the output, yk, to agree as closely as possible with desired response signal. 后來看機器學習的書，發現這與linear regression
是一樣的。通過比較系統的輸出與目標值(desired response)之間的誤差（εk = dk - yk），調整或者優化權重向量，使誤差最小。在絕
大部分的實際應用中，自適應過程都是朝着最小化均方誤差的方向進行，MSE(mean-square error)=E[εk2]=E[(dk-XkTWk)2].
均方誤差展開即為：E[εk2]=E[dk2]+WTE[XkXkT]W-2E[dkXkT]W.
Many useful adaptive process that cause the weight vector to seek the minimum of the performance surface
do so by gradient methods.
權重向量的獲取一般通過梯度方法得到，比如最速下降法：wk+1 = wk + μ*(-Δk)和牛頓法：wk+1 = wk - f(wk)/f'(wk)。The convergence
of Newton's method obviously depends on the initial guess, w0, and on the nature of f(w), but it is known to converge rapidly for a
large class of functions.
The least-mean-square algorithm, or LMS algorithm, uses a special estimate of the gradient that is valid for the adaptive linear
combiner. On the other hand, the LMS algorithm is important because of its simplicity and ease of computation, and because it
does not require off-line gradient estimations or repetitions of data. Wk+1 = Wk + 2μεkXk. If the adaptive system is an adaptive
linear combiner, and if the input vector Xk and the desired response dk are available at each iteration, the LMS algorithm is generally
the best choice for many different applications of adaptive signal processing.這個算法就是機器學習線性回歸中常用的LMS算法。
記得老師上課時候講，不要等到基礎都打好了，再去學習新知識或新的領域，基礎是永遠打不完的，等用到時候，發現缺哪些知識，再去補。