By Vladimír Olej, Petr Hájek (auth.), Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis (eds.)
th This quantity is a part of the three-volume complaints of the 20 overseas convention on Arti?cial Neural Networks (ICANN 2010) that was once held in Th- saloniki, Greece in the course of September 15–18, 2010. ICANN is an annual assembly backed via the eu Neural community Society (ENNS) in cooperation with the overseas Neural community So- ety (INNS) and the japanese Neural community Society (JNNS). This sequence of meetings has been held each year given that 1991 in Europe, masking the ?eld of neurocomputing, studying platforms and different similar components. As some time past 19 occasions, ICANN 2010 supplied a distinctive, vigorous and interdisciplinary dialogue discussion board for researches and scientists from world wide. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso the entire advancements and purposes within the zone of Arti?cial Neural Networks (ANNs). ANNs offer a data processing constitution encouraged by means of biolo- cal fearful platforms they usually include loads of hugely interconnected processing parts (neurons). every one neuron is an easy processor with a restricted computing capability usually limited to a rule for combining enter signs (utilizing an activation functionality) on the way to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the sign being communicated. ANNs have the option “to examine” by way of instance (a huge quantity of instances) via a number of iterations with no requiring a priori ?xed wisdom of the relationships among technique parameters.
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Extra info for Artificial Neural Networks – ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
The precision of computing the exact value of he parameter γ is associated to the possibility of a very accurated modifying of PTAT 2 −γ temperature dependence. In order to increase the circuit accuracy, a digitally-selected current generator will be implemented in Fig. 10. A digital word a1 − a 4 will be used for selecting the desired value of the output current temperature dependence. The expression of the currents from Fig. 10 will be: 1− 1 1 I 1 / 2 = I O I 1 = I O 2 I 12 , 1− 1 1 I 1 / 4 = I O I 1 / 2 = I O 4 I 14 , 1− 1 (7) 1 I 1 / 8 = I O I 1 / 4 = I O 8 I 18 , 1− (6) 1 (8) 1 I 1 / 16 = I O I 1 / 8 = I O 16 I 116 (9) and: I OUT 2 = 4 4 a a 1− ∑ k ∑ k k 2 2k = = k 1 k 1 IO I1 .
VDD 1 ID 1/2 ID 2IC 4IC IA IB IA Fig. 3. The current-mode square-root circuit In order to improve the circuit frequency response, only MOS transistors working in saturation will be used. The silicon occupied area is strongly reduced by replacing classical MOS devices by a FGMOS (Floating Gate MOS) transistor. The symbolic representation of the square-root circuit is shown in Fig. 4. Ia Ic Ib a b c c Ic Fig. 4. The symbolic representation of the square-root circuit After some computations, the output current of the circuit from Fig.
This is observed for all transformation parameters except for rx , which may be due a confounding inﬂuence between pitch and shearing. In fact, the Fourier coeﬃcients containing spectrum power associated to these small rotations/shearing eﬀects may be contained inside the smaller neighborhoods. Besides, it is known that the rx parameter is diﬃcult to estimate due to simmetry of the head around the x axis (left-right). In Table 1, it is also possible to see that the proposed method yields results similar to the VSB (MI) custom method mentioned above, except for rx , in which the latter yields better results, and for sz , in which one observes the opposite.
Artificial Neural Networks – ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I by Vladimír Olej, Petr Hájek (auth.), Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis (eds.)