Artificial Neural Networks. A Practical Course by Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade

By Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves

This e-book presents complete assurance of neural networks, their evolution, their constitution, the issues they could resolve, and their functions. the 1st 1/2 the publication appears to be like at theoretical investigations on synthetic neural networks and addresses the main architectures which are able to implementation in a number of software eventualities. the second one part is designed particularly for the construction of strategies utilizing synthetic neural networks to unravel functional difficulties coming up from various components of information. It additionally describes a number of the implementation info that have been taken under consideration to accomplish the said effects. those elements give a contribution to the maturation and development of experimental innovations to specify the neural community structure that's appropriate for a selected program scope. The e-book is suitable for college students in graduate and top undergraduate classes as well as researchers and professionals.

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Extra resources for Artificial Neural Networks. A Practical Course

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1. Thus, independently of the activation function in use, only two values can be produced by the network output, that is, 0 or 1 for the step function, and −1 or 1 if the bipolar step function is used. The xi inputs can assume any numeric value and depend on the problem being mapped by the Perceptron. In practice, techniques for input normalization, considering the numerical range of the adopted activation function, are also used to improve the computational performance of the training process.

2. Consider an application with four inputs and two outputs. The designers of this application state that the feedforward network to be developed must present exactly four neurons in the first hidden layer. Discuss about the pertinence of this information. 3. Relating to the previous exercise, cite some factors that influence the determination of the hidden layers number of a multiple layer feedforward network. 4. What are the eventual structural differences observed between recurrent networks and feedforward networks.

Is the activation function and u is the activation potential. x1 w1 −1 {x0} θ = w0 x2 x3 xn w2 w3 Σ wn Fig. ) – – Real or binary (from the external environment) Real (initialized with random values) Real (initialized with random values) Binary Step or bipolar step function Supervised Delta rule Since the ADALINE is still mostly used in applications involving pattern recognition, its outputs can classify only two classes, which are associated to one of the two possible values produced by the adopted activation function (step or bipolar step).

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