پرسپترون

الپرسپترون Perceptron هوأحد أبسط أنواع الشبكات العصبونية أمامية التغذية Feed-Forward ، حيث لا يحتوي على طبقة عصبونات خفية بل تنتقل المعلومات المدخلة من الطبقة الأمامية إلى النهائية مباشرة. أول من استحدث شبكة الپرسپترون كان فرانك روزنبلات عام 1957 في جامعة كورنل.

التعريف

يستخدم الپرسپترون القيم الذاتية للمصفوفات لتمثل الشبكات العصبونية أمامية التغذية وهوa tertiary classifier that maps its input ${\displaystyle f(x)$

{\displaystyle f(x)={\begin{cases 1&{\text{if w\cdot x+b>0\\0&{\text{else \end{cases

where ${\displaystyle b$

مثال

A perceptron (X₁, X₂ input, X₀*W₀=b, TH=0.5) learns how to perform a NAND function:

		المـُدخل				Initial			المـُخرج							النهائي
Threshold	معدل التفهم	Sensor values			المـُخرج المرغوب	الأوزان			المحسوب			المجموع	الشبكة	الخطأ	التسليم	الأوزان
TH	LR	X0	X1	X2	Z	w0	w1	w2	C0	C1	C2	S	N	E	R	W0	W1	W2
									X0 x w0	X1 x w1	X2 x w2	C0+C1+C2	IF(S>TH,1,0)	Z-N	LR x E
0.5	0.1	1	0	0	1	0	0	0	0	0	0	0	0	1	+0.1	0.1	0	0
0.5	0.1	1	0	1	1	0.1	0	0	0.1	0	0	0.1	0	1	+0.1	0.2	0	0.1
0.5	0.1	1	1	0	1	0.2	0	0.1	0.2	0	0	0.2	0	1	+0.1	0.3	0.1	0.1
0.5	0.1	1	1	1	0	0.3	0.1	0.1	0.3	0.1	0.1	0.5	0	0	0	0.3	0.1	0.1
0.5	0.1	1	0	0	1	0.3	0.1	0.1	0.3	0	0	0.3	0	1	+0.1	0.4	0.1	0.1
0.5	0.1	1	0	1	1	0.4	0.1	0.1	0.4	0	0.1	0.5	0	1	+0.1	0.5	0.1	0.2
0.5	0.1	1	1	0	1	0.5	0.1	0.2	0.5	0.1	0	0.6	1	0	0	0.5	0.1	0.2
0.5	0.1	1	1	1	0	0.5	0.1	0.2	0.5	0.1	0.2	0.8	1	-1	-0.1	0.4	0	0.1
0.5	0.1	1	0	0	1	0.4	0	0.1	0.4	0	0	0.4	0	1	+0.1	0.5	0	0.1
0.5	0.1	1	0	1	1	0.5	0	0.1	0.5	0	0.1	0.6	1	0	0	0.5	0	0.1
0.5	0.1	1	1	0	1	0.5	0	0.1	0.5	0	0	0.5	0	1	+0.1	0.6	0.1	0.1
0.5	0.1	1	1	1	0	0.6	0.1	0.1	0.6	0.1	0.1	0.8	1	-1	-0.1	0.5	0	0
0.5	0.1	1	0	0	1	0.5	0	0	0.5	0	0	0.5	0	1	+0.1	0.6	0	0
0.5	0.1	1	0	1	1	0.6	0	0	0.6	0	0	0.6	1	0	0	0.6	0	0
0.5	0.1	1	1	0	1	0.6	0	0	0.6	0	0	0.6	1	0	0	0.6	0	0
0.5	0.1	1	1	1	0	0.6	0	0	0.6	0	0	0.6	1	-1	-0.1	0.5	-0.1	-0.1
0.5	0.1	1	0	0	1	0.5	-0.1	-0.1	0.5	0	0	0.5	0	1	+0.1	0.6	-0.1	-0.1
0.5	0.1	1	0	1	1	0.6	-0.1	-0.1	0.6	0	-0.1	0.5	0	1	+0.1	0.7	-0.1	0
0.5	0.1	1	1	0	1	0.7	-0.1	0	0.7	-0.1	0	0.6	1	0	0	0.7	-0.1	0
0.5	0.1	1	1	1	0	0.7	-0.1	0	0.7	-0.1	0	0.6	1	-1	-0.1	0.6	-0.2	-0.1
0.5	0.1	1	0	0	1	0.6	-0.2	-0.1	0.6	0	0	0.6	1	0	0	0.6	-0.2	-0.1
0.5	0.1	1	0	1	1	0.6	-0.2	-0.1	0.6	0	-0.1	0.5	0	1	+0.1	0.7	-0.2	0
0.5	0.1	1	1	0	1	0.7	-0.2	0	0.7	-0.2	0	0.5	0	1	+0.1	0.8	-0.1	0
0.5	0.1	1	1	1	0	0.8	-0.1	0	0.8	-0.1	0	0.7	1	-1	-0.1	0.7	-0.2	-0.1
0.5	0.1	1	0	0	1	0.7	-0.2	-0.1	0.7	0	0	0.7	1	0	0	0.7	-0.2	-0.1
0.5	0.1	1	0	1	1	0.7	-0.2	-0.1	0.7	0	-0.1	0.6	1	0	0	0.7	-0.2	-0.1
0.5	0.1	1	1	0	1	0.7	-0.2	-0.1	0.7	-0.2	0	0.5	0	1	+0.1	0.8	-0.1	-0.1
0.5	0.1	1	1	1	0	0.8	-0.1	-0.1	0.8	-0.1	-0.1	0.6	1	-1	-0.1	0.7	-0.2	-0.2
0.5	0.1	1	0	0	1	0.7	-0.2	-0.2	0.7	0	0	0.7	1	0	0	0.7	-0.2	-0.2
0.5	0.1	1	0	1	1	0.7	-0.2	-0.2	0.7	0	-0.2	0.5	0	1	+0.1	0.8	-0.2	-0.1
0.5	0.1	1	1	0	1	0.8	-0.2	-0.1	0.8	-0.2	0	0.6	1	0	0	0.8	-0.2	-0.1
0.5	0.1	1	1	1	0	0.8	-0.2	-0.1	0.8	-0.2	-0.1	0.5	0	0	0	0.8	-0.2	-0.1
0.5	0.1	1	0	0	1	0.8	-0.2	-0.1	0.8	0	0	0.8	1	0	0	0.8	-0.2	-0.1
0.5	0.1	1	0	1	1	0.8	-0.2	-0.1	0.8	0	-0.1	0.7	1	0	0	0.8	-0.2	-0.1

Note: Initial weight equals final weight of previous iteration. A too high learning rate makes the perceptron periodically oscillate around the solution. A possible enhancement is to use ${\displaystyle LR^{n$ starting with n=1 and incrementing it by 1 when a loop in learning is found.

المصادر

وصلات خارجية

Chapter ثلاثة Weighted networks - the perceptron and chapter أربعة Perceptron learning of by Raúl Rojas (ISBN 978-3540605058)
Pithy explanation of the update rule by Charles Elkan
C# implementation of a perceptron
History of perceptrons
Mathematics of perceptrons
Perceptron demo applet and an introduction by examples