Advantages of Artificial Neural Networks (ANN)

  1. Problems in ANN are represented by attribute-value pairs.
  2. ANNs are used for problems having the target function, the output may be discrete-valued, real-valued, or a vector of several real or discrete-valued attributes.
  3. ANN learning methods are quite robust to noise in the training data. The training examples may contain errors, which do not affect the final output.
  4. It is used where the fast evaluation of the learned target function required.
  5. ANNs can bear long training times depending on factors such as the number of weights in the network, the number of training examples considered, and the settings of various learning algorithm parameters.

Disadvantages of Artificial Neural Networks (ANN)

  1. Hardware Dependence:
    1. Artificial Neural Networks require processors with parallel processing power, by their structure.
    2. For this reason, the realization of the equipment is dependent.
  2. Unexplained functioning of the network:
    1. This the most important problem of ANN.
    2. When ANN gives a probing solution, it does not give a clue as to why and how.
    3. This reduces trust in the network.
  3. Assurance of proper network structure:
    1. There is no specific rule for determining the structure of artificial neural networks.
    2. The appropriate network structure is achieved through experience and trial and error.
  4. The difficulty of showing the problem to the network:
    1. ANNs can work with numerical information.
    2. Problems have to be translated into numerical values before being introduced to ANN.
    3. The display mechanism to be determined will directly influence the performance of the network.
    4. This is dependent on the user's ability.
  5. The duration of the network is unknown:
    1. The network is reduced to a certain value of the error on the sample means that the training has been completed.
    2. The value does not give us optimum results.