By Chun-Xiang Li, Dong-Xiao Niu, Li-Min Meng (auth.), Fuchun Sun, Jianwei Zhang, Ying Tan, Jinde Cao, Wen Yu (eds.)

The quantity set LNCS 5263/5264 constitutes the refereed complaints of the fifth overseas Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.

The 192 revised papers awarded have been conscientiously reviewed and chosen from a complete of 522 submissions. The papers are equipped in topical sections on computational neuroscience; cognitive technological know-how; mathematical modeling of neural structures; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic tools; supervised studying; unsupervised studying; aid vector desktop and kernel tools; hybrid optimisation algorithms; desktop studying and knowledge mining; clever keep an eye on and robotics; development reputation; audio picture processinc and computing device imaginative and prescient; fault prognosis; functions and implementations; functions of neural networks in digital engineering; mobile neural networks and complicated keep watch over with neural networks; nature encouraged equipment of high-dimensional discrete info research; development acceptance and knowledge processing utilizing neural networks.

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Extra info for Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008, Proceedings, Part II

Example text

Since some presented algorithms of association rules mining based on binary are complicated to generate frequent candidate itemsets, they may pay out heavy cost when these algorithms are used to extract constrained spatial association rules. And so this paper proposes an algorithm of constrained spatial association rules based on binary, the algorithm is suitable for mining constrained association among some different spatial objects under the same spatial pattern, which uses the way of ascending value to generates frequent candidate itemsets and digital character to reduce the number of scanned transaction in order to improve the efficiency.

836 Memetic Algorithm-Based Image Watermarking Scheme . . . . . . . 845 A Genetic Algorithm Using a Mixed Crossover Strategy . . . . . . . 854 Condition Prediction of Hydroelectric Generating Unit Based on Immune Optimized RBFNN . . . . . . . . . . . . . . . . . . . . 864 Synthesis of a Hybrid Five-Bar Mechanism with Particle Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . 873 Robust Model Predictive Control Using a Discrete-Time Recurrent Neural Network .

ANN’s forecasting average errors have little swing and less than 8 percent mainly. RS-BPANN has lest forecasting errors and average errors, which are less than 3 percent usually. It is obviously that RS-BPANN can gain a higher forecasting precision. 5 shows the max-load forecasting for March 2006 with BPANN and the relative error. It can be seen that the forecasting result curve is similar to the Rough Set Combine BP Neural Network in Next Day Load Curve Forcasting 9 actual load curve. 2%. Multi-forecasting practice shows that the BPANN can gain a satisfying precision.

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