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 court cases of the eighth overseas Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers offered in all 3 volumes have been rigorously 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 help vector machines; combination versions and clustering; visible belief and trend reputation; movement, monitoring and item attractiveness; common scene research and speech acceptance; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent platforms and adaptive dynamic programming; reinforcement studying and choice making; motion and motor keep an eye on; adaptive and hybrid clever platforms; neuroinformatics and bioinformatics; info retrieval; information mining and information discovery; and common language processing.
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Additional info 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
4. The second neural networks based prediction model. The future values of the discharges y(t) can be predicted from past values of y(t). The corresponding form of prediction can be written as follows: y (t ) = f ( y (t − 1), … , y (t − d )) (2) The above neural network models are two-layer feedforward networks, with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. W is the weight matrix, and b is the bias. This network also uses tapped delay lines to store previous values of the x(t) and y(t) sequences.
2. MLP outputs for the C04 subset, after to apply cost function and random under-sampling strategies. The line in black shows the separation between the outputs of both classes. The results obtained with this subset suggest a weak learning on the cls− (when random under-sampling is applied). Nevertheless, in the rest of the subsets it seemed as though this massive elimination of samples does not affect the cls− (as it was observed in Fig. 1), but, what happen with this?. , the outputs present more irregular tendency than the cost function.
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. The time series response when both the discharge and gage height are inputs is demonstrated in Fig.
Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II by Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)