UGC NET CSE | September 2013 | Part 3 | Question: 28

Question

In a single perceptron, the updation rule of weight vector is given by

• A. $w(n+1) = w(n) + \eta [d(n)-y(n)]$
• B. $w(n+1) = w(n) - \eta [d(n)-y(n)]$
• C. $w(n+1) = w(n) + \eta [d(n)-y(n)]*x(n)$
• D. $w(n+1) = w(n) - \eta [d(n)-y(n)]*x(n)$

Answer 1

ans is C  f

Steps[edit]

1. Initialize the weights and the threshold. Weights may be initialized to 0 or to a small random value. In the example below, we use 0.
2. For each example j in our training set D, perform the following steps over the input {\displaystyle \mathbf {x} _{j}\,}and desired output {\displaystyle d_{j}\,}:
   1. Calculate the actual output:

      {\displaystyle {\begin{aligned}y_{j}(t)&=f[\mathbf {w} (t)\cdot \mathbf {x} _{j}]\\&=f[w_{0}(t)x_{j,0}+w_{1}(t)x_{j,1}+w_{2}(t)x_{j,2}+\dotsb +w_{n}(t)x_{j,n}]\end{aligned}}}

   2. Update the weights:

      {\displaystyle w_{i}(t+1)=w_{i}(t)+(d_{j}-y_{j}(t))x_{j,i}\,}, for all features {\displaystyle 0\leq i\leq n}

       

       

      for more details refer https://en.wikipedia.org/wiki/Perceptron

Comments

From which subject is this