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An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem.
The chapter discusses how gain fields are built and exploited by the nervous system, but they will probably remain a prime example of neural design serving a computational purpose.
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Hopefully, ad hoc neural designs could improve these results even further.
This paper demonstrates the effectiveness of a new character embedding designed for training CNNs and explores how neural network design may be impacted by its adoption.
This paper describes the cascade neural network design algorithm (CNNDA), a new algorithm for designing compact, two-hidden-layer artificial neural networks (ANNs).
"Convolutional neural network design" section describes how convolutional neural networks work and design considerations that must be made on account of our new embedding.
The work flow for the neural network design process has the following primary steps: Collect data.
It is full-packed with proven techniques for neural network design and optimization.
Long short term memory (LSTM) networks are a popular deep neural network design for learning tasks with sequential data.
The neural controller design is accomplished with using the classical back-propagation algorithm (CBA).
The concept is evaluated using a 64-site neural probe design and manufacturing process.
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