By Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)

The quantity set LNCS 4431 and LNCS 4432 constitutes the refereed complaints of the eighth overseas convention on Adaptive and normal Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007.

The 178 revised complete papers awarded have been rigorously reviewed and chosen from a complete of 474 submissions. The ninety four papers of the 1st quantity are geared up in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, studying, optimization and video games, fuzzy and tough platforms, simply as category and clustering. the second one quantity includes eighty four contributions regarding neural networks, help vector machines, biomedical sign and photo processing, biometrics, computing device imaginative and prescient, in addition to to regulate and robotics.

**Read or Download Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II PDF**

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**Additional resources for Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II**

**Example text**

The achieved regions of possible parameter estimate allow to obtain the neuron uncertainty in the form of the confidence interval of the model output. 8 for k = 1, . . 0 for k = 1, . . , 160. In the case of the LMS the confidence interval of the model output (Fig. 2) was calculated with the application of expression (19). Figure 3 shows the confidence interval of the model output obtained with the application of the OBE based on the expression (20). The results obtained with the LMS indicate that the neuron output uncertainty interval does not contain the system output calculated based on the nominal parameters p.

LMS vs. g. LMS [4]. can be used during the realisation of step 1. e. there exists ξ −1 (·) The estimation algorithms of linear-in-parameters models requires the system output to be described in the following form: yn(l) (k) = r(l) n (k) T (l) p(l) n + εn (k), (3) and the output error in the case of these algorithms can be defined as: (l) ε(k)(l) ˆn(l) (k). n (k) = yn (k) − y (4) Unfortunately, the application of the LMS to the parameter estimation of neurons (1) is limited by a set of restrictive assumptions.

Int. Journal of Applied Mathematics and Computer Science. 12 (2001) 523–531 3. : Static and Dynamic Neural Networks, John Wiley & Sons, New Jersey (2003) 4. : Self-organizing of Nets of Active Neurons. System Analysis Modelling Simulation. 20 (1996) 93–106 5. , Cholewa, W. ): Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer-Verlag, Berlin (2004) 6. , Walter, E. ): Bounding Approaches to System Identification, Plenum Press, New York (1996) 7. D. Thesis, University of Zielona G´ora, Zielona G´ora (2004) (In Polish) 8.