Question
a.
na(i)
b.
n^
c.
^a(i)
d.
none of the mentioned
Posted under Neural Networks
Engage with the Community - Add Your Comment
Confused About the Answer? Ask for Details Here.
Know the Explanation? Add it Here.
Q. If a(i) is the input, ^ is the error, n is the learning parameter, then how can weight change in a perceptron model be represented?
Similar Questions
Discover Related MCQs
Q. What is adaline in neural networks?
View solution
Q. Who invented the adaline neural model?
View solution
Q. What was the main point of difference between the adaline and perceptron model?
View solution
Q. In adaline model what is the relation between output and activation value(x)?
View solution
Q. What is the another name of weight update rule in adaline model based on its functionality?
View solution
Q. In neural how can connectons between different layers be achieved?
View solution
Q. Connections across the layers in standard topologies and among the units within a layer can be organised?
View solution
Q. What is an instar topology?
View solution
Q. What is an outstar topology?
View solution
Q. The operation of instar can be viewed as?
View solution
Q. The operation of outstar can be viewed as?
View solution
Q. If two layers coincide and weights are symmetric(wij=wji), then what is that structure called?
View solution
Q. Heteroassociative memory can be an example of which type of network?
View solution
Q. What is STM in neural network?
View solution
Q. What does STM corresponds to?
View solution
Q. What LTM corresponds to?
View solution
Q. On what parameters can change in weight vector depend?
View solution
Q. If the change in weight vector is represented by ∆wij, what does it mean?
View solution
Q. What is learning signal in this equation ∆wij= µf(wi a)aj?
View solution
Q. State whether Hebb’s law is supervised learning or of unsupervised type?
View solution
Suggested Topics
Are you eager to expand your knowledge beyond Neural Networks? We've curated a selection of related categories that you might find intriguing.
Click on the categories below to discover a wealth of MCQs and enrich your understanding of Computer Science. Happy exploring!
