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It can be easily seen that many neural networks are special cases of system (1).
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem.
Many neural networks of artificial or natural origin, including the brain net, have a ring structure [1].
On the other hand, as a source of instability and poor performance, time-delays [7 9] always appear in many neural networks.
However, many neural networks with discontinuous neuron activation functions appear in the theoretical study on dynamics of neural networks, see [28, 29].
In the coupled nonlinear dynamical systems, many neural networks may experience abrupt changes in their structure and parameters caused by some phenomena such as component failures or repairs, changing subsystem interconnections, and abrupt environmental disturbances.
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Furthermore, many neural network approaches have been done to this problem.
The ANN software available today provides many neural network architectures and learning algorithms, to be applied to specific problems.
Key aspects of many neural network models are that they are able to learn and their behavior improves with training or experience.
Many neural network algorithms, which are often employed as models of cognitive processes, fail to meet all these requirements.
The available ANN software today provides many neural network architectures and learning algorithms, and it also helps users to apply ANN to their specific problems easily [12, 13].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com