► Gradual corruption: A network slows over time and undergoes relative degradation. The network problem does not immediately corrode immediately.
► Having a distributed memory: In order 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 success 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 false output ► Having fault tolerance: Corruption of one or more cells of ANN does not prevent it from generating output. This feature makes the networks fault tolerant. ► Ability to work with incomplete knowledge : After ANN training, the data may produce output even with incomplete information. The loss of performance here depends on the importance of the missing information. ► 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 prevent the network from functioning. Advantages of Artificial Neural Networks ( ANN) Figure 1: Layers of the Artificial Neural NetworkĪs shown in Figure 1, a neural network consists of three layers: an input layer, an intermediate layer and an output layer. The blue boxes shown here represent the neurons and the arrows represent the connection points. The data set prepared for training at the input layer is shown to the network. The network assigns the weights of the events it learns to the connection points in the intermediate layer. Not every point has to be a value, and some points can be zero. A threshold value is added between the layers so that the zero values at the connection points do not become zero.
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