In a single perceptron, the updation rule of weight vector is given by
A. W(n + 1)=w(n)+η[d(n)-y(n)]
B. W(n + 1) =w(n)–η[d(n)-y(n)]
C. w(n + 1)=w(n)+η[d(n)-y(n)]* x (n)
D. w(n + 1)=w(n)–η[d(n)-y(n)]* x (n)
Answer:C
In a single perceptron, the updation rule of weight vector is given by
A. W(n + 1)=w(n)+η[d(n)-y(n)] B. W(n + 1) =w(n)–η[d(n)-y(n)] C. w(n + 1)=w(n)+η[d(n)-y(n)]* x (n) D. w(n + 1)=w(n)–η[d(n)-y(n)]* x (n)