Lecture18
Perceptron Learning Rule
For the perceptron learning rule, the learning signal is the difference between the desired and actual neuron s response (Rosenblatt 1958).
is supervised and the learning signal is equal to
Thus, learning where oi = sgn(w:x), and d, is the desired response as shown in the lecture.
Weight adjustments in this method, Aw, and Awg, are obtained as follows
Aw, = c [di - sgn (wix)] x
Note that this rule is applicable only for binary neuron response, and the relationships express the rule for the bipolar binary case. Under this rule, weights are adjusted if and only if oi is incorrect. Error as a necessary condition of learning is inherently included in this training rule. Obviously, since the desired
response is either 1 or - 1, the weight adjustment reduces
where a plus sign is applicable when di = 1, and sgn (wtx) = - 1, and a minus sign is applicable when di = - 1, and sgn (wtx) = 1. The reader should notice that the weight adjustment formula cannot be used when
di = sgn(wfx).
The weight adjustment is inherently zero when the desired and actual responses agree. As we will see throughout this text, the perceptron learning rule is of central importance for supervised learning of neural networks. The weights are initialized at any values in this method
Delta Learning Rule
The delta learning rule is only valid for continuous activation functions as defined before, and in the supervised training mode. The learning signal for this rule is called delta and is defined as follows:
Download the lecture.
This example discusses the delta learning rule as applied to the network shown in Figure . Training input vectors, desired responses, and initial weights are identical to those in the previous Example . The delta learning requires
that the value f (net) be computed in each step. If you work on the unipolar activation function ,the derivative of f(net) will be changed to
f (net) =o(1-o)
,and in the same way the value f (net) will be computed in each step
المادة المعروضة اعلاه هي مدخل الى المحاضرة المرفوعة بواسطة استاذ(ة) المادة . وقد تبدو لك غير متكاملة . حيث يضع استاذ المادة في بعض الاحيان فقط الجزء الاول من المحاضرة من اجل الاطلاع على ما ستقوم بتحميله لاحقا . في نظام التعليم الالكتروني نوفر هذه الخدمة لكي نبقيك على اطلاع حول محتوى الملف الذي ستقوم بتحميله .