By Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed lawsuits of the eighth overseas Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers awarded in all 3 volumes have been conscientiously reviewed and chosen from 651 submissions. The contributions are dependent in topical sections on computational neuroscience and cognitive technological know-how; neurodynamics and complicated platforms; balance and convergence research; neural community types; supervised studying and unsupervised studying; kernel equipment and aid vector machines; mix versions and clustering; visible belief and trend popularity; movement, monitoring and item attractiveness; average scene research and speech popularity; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent platforms and adaptive dynamic programming; reinforcement studying and selection making; motion and motor regulate; adaptive and hybrid clever platforms; neuroinformatics and bioinformatics; info retrieval; facts mining and information discovery; and ordinary language processing.
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Extra resources for Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II
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ISNN 2011, Part II, LNCS 6676, pp. 27–36, 2011. © Springer-Verlag Berlin Heidelberg 2011 28 N. Zhang include neural networks , fuzzy logic , evolutionary algorithm , support vector machine , particle swarm optimization , or the combination of them . Comparatively, various runoff forecast models based on neural networks perform much better in accuracy than many conventional prediction models. However, a fact could not be neglected that most of the existing computational intelligence based models have not yet satisfied researchers in forecast precision.
In comparison, if gage height was used as the only input/target, the error autocorrelation function is plotted in Fig. 8. Fig. 7. Error autocorrelation function when the input is gage height, and the target is discharge. It describes how the prediction errors are related in time. Fig. 8. Error autocorrelation function when the only input is gage height. It describes how the prediction errors are related in time. 34 N. 4 Time Series Response A comparative study was performed between the case when the discharge and gage height are inputs, and when the gage height is used as the only input.