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An artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving complex problems.
Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems.
Artificial Neural Network (ANN)s are trained by back propagating the error derivatives and therefore have the potential to learn much better models of nonlinear data.
The artificial neural network technique is powerful because it can learn to represent complicated data patterns or data relationships between input and output variables of the system being studied.
When exposed to complex data sets, artificial neural networks (ANNs) can recognize patterns, learn the relationship of different variables, and address classification tasks.
Artificial Neural Networks have proven to be a very powerful machine learning algorithm which can be adequate to learn successfully a variety of tasks.
Neural network systems can be made to learn faster and generalize better through the addition of knowledge.
Open accessJournal ArticleDOI
Elena Vlamou, Basil K. Papadopoulos 
11 Oct 2019
31 Citations
By adapting the interconnections between layers, Artificial Neural networks are able to learn.
It is shown that modifying the sigmoidal basis function of a multi-layer feedforward artificial neural network using a control parameter improves the network's ability to learn.
By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.

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