Home > Artificial Intelligence > Quizzes > Neural Network Practice Test: Competitive Learning Neural Networks
Neural Network Practice Test: Competitive Learning Neural Networks
Fast practice, instant feedback. Timer auto-submits when time’s up.
Avg score: 88% • Most missed: “Which layer has feedback weights in competitive neural networks?”
This quiz covers: Competitive Learning Neural Networks, feedback layer and feature mapping network analysis. Competitive learning is a type of unsupervised learning in artificial neural networks. It involves a network of artificial neurons competing to become active in response to a specific input. The "winning" neuron is then updated, while the others are left unchanged.  Competitive learning is also known as the Winner-takes-All rule. In this rule, all the output nodes compete with each other to represent the input pattern. The winner is the node with the most outputs and is given the... Show more
Neural Network Practice Test: Competitive Learning Neural Networks
Time left 00:00
25 Questions

1. What is true for competitive learning?
2. How is feature mapping network distinct from competitive learning network?
3. What is the nature of general feedback given in competitive neural networks?
4. If a competitive network can perform feature mapping then what is that network can be called?
5. What is an instar?
6. What conditions are must for competitive network to perform feature mapping?
7. What is true regarding adaline learning algorithm
8. What conditions are must for competitive network to perform pattern clustering?
9. How are input layer units connected to second layer in competitive learning networks?
10. What is the other name of feedback layer in competitive neural networks?
11. By normalizing the weight at every stage can we prevent divergence?
12. What consist of competitive learning neural networks?
13. In self organizing network, how is layer connected to output layer?
14. The weight change in plain hebbian learning is?
15. what kind of feedbacks are given in competitive layer?
16. In feature maps, when weights are updated for winning unit and its neighbour, which type learning it is known as?
17. In pattern clustering, does physical location of a unit relative to other unit has any significance?
18. Which layer has feedback weights in competitive neural networks?
19. In competitive learning, node with highest activation is the winner, is it true?
20. Generally how many kinds of pattern storage network exist?
21. Use of nonlinear units in the feedback layer of competitive network leads to concept of?
22. What is the objective of feature maps?
23. An instar can respond to a set of input vectors even if its not trained to capture the behaviour of the set?
24. What is the nature of weights in plain hebbian learning?
25. What kind of learning is involved in pattern clustering task?

⚡ Recently practiced quizzes in this topic
Live quiz activity
Artificial Intelligence Practice Test Questions: Adversarial Search Natural Language Processing (NLP) Basics Test Soft Computing Practice Test Questions Artificial Intelligence Fundamentals: Introduction to Artificial Intelligence Practice Test Artificial Intelligence: Search Techniques CBSE Class 10 Natural Language Processing AI-417 Practice Test Artificial Intelligence Practice Test Questions: Planning and Acting in the Real World Artificial Intelligence & Soft Computing: Expert System Neural Network Practice Test: Feedforward Neural Networks Artificial Intelligence Practice Test Questions 3
 
❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

🌈  Our mission is to help improve your scores in any subject and exam withonline quizzes, practice tests, study guides, & advice for students. Since 2018.
About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know?
Privacy | Terms |
Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com

All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.