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 publication presents entire assurance of neural networks, their evolution, their constitution, the issues they could resolve, and their purposes. the 1st half the e-book appears to be like at theoretical investigations on synthetic neural networks and addresses the most important architectures which are able to implementation in a variety of program eventualities. the second one part is designed particularly for the construction of options utilizing man made neural networks to unravel useful difficulties bobbing up from assorted parts of information. It additionally describes many of the implementation info that have been taken under consideration to accomplish the stated effects. those facets give a contribution to the maturation and development of experimental suggestions to specify the neural community structure that's the best option for a selected software scope. The ebook is acceptable for college students in graduate and top undergraduate classes as well as researchers and professionals.

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The Kohonen network is the main representative of mesh architectures, and its training is performed through a competitive process, as will be described in the following chapters. 4 illustrates an example of the Kohonen network where its neurons are arranged within a two-dimensional space. 2 Main Architectures of Artificial Neural Networks Fig. 4 Structure of a mesh network 25 x1 x2 xn From Fig. 4, it is possible to verify that in this network category, the several input signals are read by all neurons within the network.

However, in contrast to the step function, the logistic function is fully differentiable in its entire definition domain. 16 1 Introduction g(u) Fig. 8 The logistic activation function 1 Inflection point β u g(u) Fig. 9 Influence of the parameter b in the logistic activation function 1 β increasing u (b) Hyperbolic tangent function The output result, unlike the case of the logistic function, will always assume real values between −1 and 1, with the following mathematical expression: gðuÞ ¼ 1 À eÀbÁu , 1 þ eÀbÁu ð1:9Þ where b is also associated with the slope of the hyperbolic tangent function in its inflection point.

8. A problem of pattern classification is linearly separable and has 50 samples. In a given training epoch, it was observed that the network was not producing the desired output for only one sample. Is it necessary to present all the 50 samples on the next training epoch? 9. 25 A B B A Is it possible to apply the Perceptron for solving this problem? 10. Explain in details what possible limitations the Perceptron would meet if its activation threshold was null {h = 0}. 6 Practical Work The analysis of a fractional distillation process for petrol revealed that a given oil could be classified into two purity classes {P1 and P2} from the measurement of three variables {x1, x2 and x3}, which represent some physicochemical properties of the oil.

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