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What are the advantages and disadvantages of Artificial Neural Networks?
An artificial neural network is a system located on the services of biological neural networks. It is a simulation of a biological neural system. The characteristic of artificial neural networks is that there are multiple architectures, which consequently needed several methods of algorithms, but despite being a complex system, a neural network is nearly simple.
These networks are among the unique signal-processing technologies in the director’s toolbox. The area is highly interdisciplinary, but this method will restrict the look to the engineering outlook.
In engineering, neural networks deliver two important functions as pattern classifiers and as non-linear adaptive filters. An Artificial Neural Network is a flexible, most often non-linear system that understands to implement a function (an input/output map) from data. Adaptive defines that the system parameters are transformed during operation, generally known as the training phase.
After the training phase, the Artificial Neural Network parameters are constant and the system is set up to solve the issue at hand (the testing phase). The Artificial Neural Network is developed with a systematic step-by-step phase to enhance a performance test or to follow some implicit internal constraint, which is generally defined as the learning rule.
The input/output training data are fundamental in neural network technology because they transmit the important data to “discover” the optimal operating point. The non-linear characteristics of the neural network processing elements (PEs) supports the system with several adaptability to obtain virtually some desired input/output map, i.e., some artificial neural networks are extensive mapmakers.
An input is displayed to the neural network and an equivalent desired or target response is set at the output (when this is the case the training is known as supervised). An error is collected from the difference between the acquired response and the system output. This error data is delivered back to the system and systematically regulates the system parameters (the learning rule). The phase is repeated until the performance is adequate. It is clear from this definition that the performance hinges thickly on the data.
Advantages of Artificial Neural Network
The advantages of the neural network are as follows −
A neural network can implement tasks that a linear program cannot.
When an item of the neural network declines, it can continue without some issues by its parallel features.
A neural network determines and does not require to be reprogrammed.
It can be executed in any application.
Disadvantages of Artificial Neural Network
The disadvantages of the neural network are as follows −
The neural network required training to operate.
The structure of a neural network is disparate from the structure of microprocessors therefore required to be emulated.
It needed high processing time for big neural networks.
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