Advanced Computer Systems: Eighth International Conference, by Gisella Facchinetti (auth.), Jerzy Sołdek, Jerzy Pejaś

By Gisella Facchinetti (auth.), Jerzy Sołdek, Jerzy Pejaś (eds.)

Advanced desktop Systems is a set of 40 chosen papers provided to the 8th foreign convention on desktops, October 2001 in Mielno, Poland. those papers offer a entire precis of perform and examine growth in info applied sciences:

  • Recognition, safety and security concentrates at the widely-known difficulties of data structures security.
  • Methods of man-made Intelligence offers tools and algorithms that are the fundamentals for the purposes of man-made intelligence environments.
  • Intelligent brokers and dispensed actions contains laboratory study on multiagent clever platforms in addition to upon their functions in looking details, negotiating and assisting decision.
  • Distributed Productions Networks and Modeling complicated platforms current construction techniques in dispensed shared digital surroundings, digital resolution of integer optimization difficulties, and a queuing method of functionality optimization within the allotted creation network.

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Extra resources for Advanced Computer Systems: Eighth International Conference, ACS ‘2001 Mielno, Poland October 17–19, 2001 Proceedings

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I I o Fig. 5 The set of such fuzzy values may be considered as T; terms of the linguistic variable (LV) with the name corresponding to y;{t): The set of m linguistic variables (where m is dimension of Y(tJ vector) may be named as a fuzzy trajectory: Similarly, the reference fuzzy trajectory may be constructed: For computing the distance apart fuzzy trajectories the equation may be suggested as follows: p(TR, TR*) i = I,m; = 1j rnfn(tnjn {u~ (T; ,(T;)· )), = I,n. In this equation the jlf describes the likeness of j-th terms of reference and real trajectories' i-th LV (Fig.

Each sample represents real data used by bank with 51 inputs which are continuous, nominal with small numbers of values and nominal with larger numbers of values, and the output which describes whether the bank granted the credit card (1) or not (0). The whole data set includes 690 examples. As in the previous example, the best value of tuning coefficient c will be found with the leave one out crossvalidation method. The number of wrong classified samples in crossvalidation process with respect to the coefficient c is presented in Fig.

The probabilistic REF neural network does not posses all of the mentioned above disadvantages. It has only ~ coefficient to tune so its learning is very easy and much faster than the feedforward multilayer network. One of the probabilistic network faults is that it has very strong tendency for overfitting, so tuning of its coefficient must be realised with the usage of test data or crossvalidation techniques. 2. PROBABILISTIC RBF NEURAL NETWORK The probabilistic REF neural network [2, 3] works with data set (file) which acts as a learning set for the feedforward neural network.

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