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Location queries are ooded on the network and the reply message will contain the current geographical position of a given vehicle By means of simulation, this research work compares the GSR approach with nonposition-based ad hoc routing strategies such as the Dynamic Source Routing (DSR) and Ad Hoc On demand Distance Vector Routing (AODV) [224] While the DSR s performance is severely affected by issues such as scalability and high mobility (eg short route lifetimes), both AODV and the GSR position-based approaches have good performance, with the position-based approach outperforming AODV The DSR protocol has the highest network load among all three protocols as it generates a large amount of signaling traf c This protocol sends large packets due to the routes appended to the packet headers, especially during the route discovery phase, which leads to a signi cant network overload.

Such networks are assumed to experience frequent, long duration partitioning and may never have an end-to-end path This is a niche scenario since few applications will tolerate high delay for web access and other instant-based services 3746 Added-value services We observe that there is a potential for using vehicle-to-vehicle communication to leverage a class of opportunistic communication services that, although non-safety critical, could be realized with an IVC network We include in this list services such as data look-up for le sharing among moving vehicles..

The second cause of failure for DSR is that mobility of vehicles cause frequent route breaks This is more severe in highway scenarios which have higher mobility than city scenarios On the other hand, the GSR position-based approach shows a slightly higher packet delivery rate when compared to DSR and AODV The GSR and DSR show that there.

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6421 Center Adaptation All the centers are associated with a Gaussian function, the width of which for all the functions is set as the Euclidean distance between the two centers that are separated the most from each other The closest center to a particular input vector is moved toward the input vector by a percentage a of the present distance between them a is initialized to 20 every time that the ANN is retrained, and its value is decreased linearly with the number of iterations until a becomes 0; then the ANN is trained for a number of iterations (eg, between 10 and 30 iterations for the entire training data set, depending on the time left for the training) in order to obtain the most satis ed weights for the nal value of the centers.

Fig. 8.3 Cyclic voltammogram for Ba2+ in DMF-50 M Hex4NBF4 obtained at a semispherical Hg electrode at 23 V s 1 (Izutsu, Okamura, unpublished results).

The thresholds that determine the centers and weight adaptation are determined empirically 6422 Weight Adaptation The delta rule [equation (B9) in Appendix B] is used to adapt the weighted connections from the centers to the output neurons [11] In particular, for each pair of input and desired output vectors presented, one adaptation step, according to the delta rule, is made A new center can be inserted between the most distant center C (which can be determined by Euclidean distance) and the center closest to it when the average error in the training data set does not fall by more than 10% after 10 iterations (of the entire training set) Centers are also eliminated if the absolute value of the weight associated with a neuron is less than 20% of the average absolute value computed on the ve smallest weights.

9.6 INFINITE-STATE M/M/c QUEUE Consider a birth and death process on states 0, 1, 2, . . . with transitions only between neighboring states. Let the transmittances be de ned as Ti,i+1 (s) = pi,i+1 Mi,i+1 (s) Ti,i 1 (s) = pi,i 1 Mi,i 1 (s). (9.35)

The number of neurons in the middle layer is kept above 20 This is a simple and ef cient way of reducing the size of the ANN without decreasing its memory dramatically After the adaptation of the ANN is complete, a more satisfactory solution (ie, a crisp value) for the problem can be obtained, but in practice, this solution is hardly completely accurate The next step is therefore to make some adaptation of the solution Since this is a real-time problem, it is impossible to evaluate the outcome of the system before it is used To improve this situation to some extent, an error limit can be de ned to substitute the crisp output with a band (or error interval) around the output of the ANN For example, if y is the crisp solution obtained.

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