Welcome to the Feedback Neural Networks MCQs Page

Dive deep into the fascinating world of Feedback Neural Networks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Feedback Neural Networks, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Feedback Neural Networks, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Neural Networks.


Check out the MCQs below to embark on an enriching journey through Feedback Neural Networks. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Neural Networks.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Feedback Neural Networks. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Feedback Neural Networks MCQs | Page 1 of 8

How can false minima be reduced in case of error in recall in feedback neural networks?
Answer: (b).by using probabilistic update
Answer: (b).A feedback network with hidden units and probabilistic update
Answer: (a).to associate a given pattern with itself
Is there any error in linear autoassociative networks?
Answer: (b).NO
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output in case of autoassociative feedback network?
Answer: (b).a(l) + e
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is accretive in nature?
Answer: (a).a(l)
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is interpolative in nature?
Answer: (b).a(l) + e
What property should a feedback network have, to make it useful for storing information?
Answer: (a).accretive behaviour
Answer: (c).both to store and recall
Linear neurons can be useful for application such as interpolation, is it true?
Answer: (a).YES
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