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What are the characteristics of ANN?
An artificial neural network is a system placed on the functions of biological neural networks. It is a simulation of a biological neural system. The feature of artificial neural networks is that there are several structures, which required several approaches of algorithms, but regardless of being a complex system, a neural network is easy.
These networks are between the specific signal-processing sciences in the director’s toolbox. The space is hugely interdisciplinary, but this technique will restrict the view to the engineering viewpoint.
The input/output training data are essential in neural network technology because they send the essential record to “find” the optimal operating point. The non-linear features of the neural network processing elements (PEs) provide the system with multiple adaptabilities to acquire virtually several desired input/output maps, i.e., some artificial neural networks are broad mapmakers.
Input is shown to the neural network and the same desired or focus response is set at the output (when this is the method the training is called supervised).
An error is composed of the difference between the captured response and the system output. This error record is delivered back to the system and consistently manages the system parameters (the learning rule). The process is repeated until the performance is efficient. It is free from this representation that the performance hinges thickly on the information.
There are the following characteristics of Artificial Neural Network which are as follows −
Multilayer neural networks with a minimum of one hidden layer are universal approximates. It can be used to approximate some target functions. Because an ANN has a very expressive hypothesis area, it is essential to select the appropriate network topology for a given problem to prevent model overfitting.
ANN can manage redundant features because the weights are necessarily learned during the training phase. The weights for redundant features tend to be small.
Neural networks are susceptible to the presence of noise in the training information. There is one method to handle noise is to need a validation set to decide the generalization error of the model. Another method is to reduce the weight by some element at each iteration.
The gradient descent approach is used for learning the weights of an ANN assembled to some local minimum. One method to escape from the local minimum is to insert a momentum term into the weight update formula.
Training an ANN is a slow process, particularly when the multiple hidden nodes are high. However, test instances can be defined increasingly.
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