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Would I be right in saying a neural network is good at finding 'good enough' solutions for a problem?

I'm thinking this because they don't provide a binary output for a given input but a probability, for example, 0.67 could be output.

I'm also guessing because they're often used for generalization they're good at find solutions that often solve the problem but in some rare cases won't.

Thank you!

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Here are some advantages of Artificial Neural Networks ( ANN)

  • Storing information on the entire network: Information such as in traditional programming is stored on the entire network, not on a database. The disappearance of a few pieces of information in one place does not restrict the network from functioning. 

  • The ability to work with inadequate knowledge: After ANN training, the data may produce output even with incomplete information. The lack of performance here depends on the importance of the missing information. 

  • It has fault tolerance:  Corruption of one or more cells of ANN does not prevent it from generating output. This feature makes the networks fault-tolerant. 

  • Having a distributed memory: For ANN to be able to learn, it is necessary to determine the examples and to teach the network according to the desired output by showing these examples to the network. The network's progress is directly proportional to the selected instances, and if the event can not be shown to the network in all its aspects, the network can produce incorrect output 

  • Gradual corruption:  A network slows over time and undergoes relative degradation. The network problem does not immediately corrode.

  • Ability to train machine: Artificial neural networks learn events and make decisions by commenting on similar events. 

  •  Parallel processing ability:  Artificial neural networks have numerical strength that can perform more than one job at the same time. 

Disadvantages of Artificial Neural Networks (ANN)

  • Hardware dependence:  Artificial neural networks require processors with parallel processing power, by their structure. For this reason, the realization of the equipment is dependent. 

  • Unexplained functioning of the network: This is the most important problem of ANN. When ANN gives a probing solution, it does not give a clue as to why and how. This reduces trust in the network. 

  • Assurance of proper network structure:  There is no specific rule for determining the structure of artificial neural networks. The appropriate network structure is achieved through experience and trial and error. 

  • The difficulty of showing the problem to the network:  ANNs can work with numerical information. Problems have to be translated into numerical values before being introduced to ANN. The display mechanism to be determined here will directly influence the performance of the network. This depends on the user's ability. 

  • The duration of the network is unknown: The network is reduced to a certain value of the error on the sample means that the training has been completed. This value does not give us optimum results. 

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