Explore topic-wise MCQs in Neural Network.

This section includes 2 Mcqs, each offering curated multiple-choice questions to sharpen your Neural Network knowledge and support exam preparation. Choose a topic below to get started.

1.	If e(m) denotes error for correction of weight then what is formula for error in perceptron learning model: w(m + 1) = w(m) + n(b(m) s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and w denotes weight
A.	e(m) = n(b(m) s(m)) a(m)
B.	e(m) = n(b(m) s(m))
C.	e(m) = (b(m) s(m))
D.	none of the mentioned
Answer» D. none of the mentioned

Discussion

2.	w(m + 1) = w(m) + n(b(m) s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and w denotes weight, can this model be used for perceptron learning?
A.	yes
B.	no
Answer» B. no

Discussion

Explore topic-wise MCQs in Neural Network.

If e(m) denotes error for correction of weight then what is formula for error in perceptron learning model: w(m + 1) = w(m) + n(b(m) s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and w denotes weight

w(m + 1) = w(m) + n(b(m) s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and w denotes weight, can this model be used for perceptron learning?