Question

Which of the following is not the promise of an artificial neural network?

a. It can explain result

b. I can survive the failure of some nodes

c. It has inherent parallelism

d. I can handle noise
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Answer 1

Answer:

i can handle noise

Explanation :hope this ans was helpfull  so pls like

Answer 2

Answer:

Option a - It can explain result is the correct option. An artificial neural network is not capable of explaining the result.

Explanation:

Artificial neural network:

• A neural network (artificial neuron network) is a computational model that simulates the way nerve cells in the brain work.
• Artificial neural networks (ANNs) make use of learning algorithms that may make adjustments - or learn - on their own as fresh information is received.

Advantages:

• Storing data on the entire network: Information is saved on the entire network, not in a database, as it is in traditional programming. The network continues to function despite the loss of a few pieces of information in one location.
• Ability to work with incomplete knowledge: After ANN training, the data can provide output even when the information is incomplete. The amount of performance lost here is determined by the relevance of the missing data.
• Fault tolerance: If one or more ANN cells are corrupted, the ANN continues to generate output. The networks become fault resistant as a result of this characteristic.
• Parallel processing capability: Artificial neural networks have the numerical power to perform multiple tasks at once.

Disadvantages:

• Hardware dependence: Artificial neural networks are hardware-dependent due to their structure, which necessitates processors with parallel processing power. As a result, the equipment's manifestation is contingent.
• Unexplained behaviour: The most serious issue with ANN is the network's unexplained behaviour. When ANN generates a probing solution, it doesn't explain why or how. The network's trust is eroded as a result of this.
• Difficulty in presenting the problem to the network: ANNs are capable of working with numerical data. Before introducing ANN to a problem, it must be transformed into numerical values. The display mechanism chosen here will have a direct impact on the network's performance. This is dependent on the user's skill level.
• The network's duration is unknown: When the network's error on the sample is decreased to a specific value, the training is complete. This value does not get the best outcomes.

Hence, ANN cannot explain the result. Therefore, Option (a) is not a feature of an artificial neural network.

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