Get Artificial Intelligence and Neural Networks: 14th Turkish PDF

By F. Acar Savaci

ISBN-10: 3540367136

ISBN-13: 9783540367130

This booklet constitutes the completely refereed post-proceedings of the 14th Turkish Symposium on man made Intelligence and Neural Networks, TAINN 2005, held in Izmir, Turkey in June 2005.

From seventy five submissions, 26 revised complete papers handed rounds of reviewing and revision and have been ultimately chosen for inclusion within the booklet. The papers are labeled in topical sections on robotics, picture processing, category, studying conception and help vector machines, fuzzy neural networks, robotics, fuzzy good judgment, computer studying, engineering functions, and neural networks architecture.

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Extra info for Artificial Intelligence and Neural Networks: 14th Turkish Symposium, TAINN 2005, Izmir, Turkey, June 16-17, 2005, Revised Selected Papers

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5γsup . The dashed lines on each plot shows ideal partitioning. K. M¨uezzino˘glu Observe from Figure 4 that the widths of the RBFs impacts also the partitioning of the pattern space, even after all are set equal to γ. Choosing a small γ in the design is advantageous from this point of view, too. 5γsup ), it can be concluded that the negative effect of nonzero γ becomes more visible when the number of prototypes is fewer. Therefore, a large prototype set would more likely be handled better by the dynamical NNC from this aspect, too.

M (1) are dense in the space of continuous functions defined on hypercube or compact set, wji output layer weight values, θi , θj threshold values and pi input to neuron. ) is called the sigmoid activation function and N is number of units (also called nodes and neurons) in the hidden layer. )is the output of the ith sigmoid function. ) to each element of the vector p. ) for each neuron are not necessary the same. Finally, εj(x) is called as the neural network functional reconstruction error. In general, even given the best possible weight values, the given nonlinear function is not exactly approximated and functional reconstruction errors εj(x) are remaining.

1986) 21. : Robot Vision. McGraw-Hill, New York (1986) 22. Gill P. , 1981 23. D. tr 2 Kocaeli University, Industrial Electronics Dept. tr Abstract. In this paper, a control algorithm based on neural networks is presented. This control algorithm has been applied to a robot arm which has a highly nonlinear structure. The model based approaches for robot control require high computational time and can result in a poor control performance, if the specific model structure selected does not properly reflect all the dynamics.

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Artificial Intelligence and Neural Networks: 14th Turkish Symposium, TAINN 2005, Izmir, Turkey, June 16-17, 2005, Revised Selected Papers by F. Acar Savaci


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