文件名称:InTech-Direct_neural_network_control_via_inverse_
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Applications of Artificial Neural Networks (ANNs) attract the attention of many scientists
from all over the world. They have many advantages over traditional algorithmic methods.
Some of these advantages are, but not limited to ease of training and generalization,
simplicity of their architecture, possibility of approximating nonlinear functions,
insensitivity to the distortion of the network and inexact input data-Applications of Artificial Neural Networks (ANNs) attract the attention of many scientists
from all over the world. They have many advantages over traditional algorithmic methods.
Some of these advantages are, but not limited to ease of training and generalization,
simplicity of their architecture, possibility of approximating nonlinear functions,
insensitivity to the distortion of the network and inexact input data
from all over the world. They have many advantages over traditional algorithmic methods.
Some of these advantages are, but not limited to ease of training and generalization,
simplicity of their architecture, possibility of approximating nonlinear functions,
insensitivity to the distortion of the network and inexact input data-Applications of Artificial Neural Networks (ANNs) attract the attention of many scientists
from all over the world. They have many advantages over traditional algorithmic methods.
Some of these advantages are, but not limited to ease of training and generalization,
simplicity of their architecture, possibility of approximating nonlinear functions,
insensitivity to the distortion of the network and inexact input data
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InTech-Direct_neural_network_control_via_inverse_modelling_application_on_induction_motors.pdf