Question

define feature of ANN with one advantage and one disadvantage​

Answer 1

Answer:

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. 

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

Answer 2

Answer:

Advantages of Artificial Neural Networks

Artificial neural networks have the ability to provide the data to be processed in parallel, which means they can handle more than one task at the same time. Artificial neural networks have been in resistance.

Disadvantages of Artificial Neural Networks (ANN)

► Hardware dependence: Artificial neural networks require processors with parallel processing power, in accordance with their structure. ... ► Difficulty of showing the problem to the network: ANNs can work with numerical information.

Artificial neural networks, usually simply called neural networks, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.

Features in a neural network are the variables or attributes in our data set. We usually pick a subset of variables that can be used as good predictors by your model. So in a neural network, the features would be the input layer, not the hidden layer nodes

Explanation:

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