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 lawsuits of the fifth overseas Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.
The 192 revised papers awarded have been rigorously 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 platforms; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic tools; supervised studying; unsupervised studying; help vector laptop and kernel tools; hybrid optimisation algorithms; computing device studying and knowledge mining; clever regulate and robotics; development acceptance; audio photograph processinc and desktop imaginative and prescient; fault prognosis; functions and implementations; functions of neural networks in digital engineering; mobile neural networks and complex keep an eye on with neural networks; nature encouraged tools of high-dimensional discrete information research; trend popularity and data processing utilizing neural networks.
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Additional resources for Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008, Proceedings, Part II
Generalized Multiscale Radial Basis Function Networks. Neural Networks 20, 1081–1094 (2007) 18. : Weather Sensitive Short-Term Load Forecasting Using Knowledge-Based ARX Models. IEEE Power Engineering Society General Meeting 1, 1190–1196 (2005) 19. : Fast k nearest neighbour search for R-tree family. In: International Conference on Information Communications and Signal Processing ICICS 1997, vol. 2, pp. cn Abstract. Based on the principle of fuzzy clustering analysis and the theory of entropy, an improved fuzzy clustering method is given by improving the method of establishing the membership function, combining the clustering weight with the entropy coefficient, and replacing the Zadeh operator M( , ) with the weight average operator M(+,•).
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 . . . . . . . . . . . . .
883 A PSO-Based Method for Min-ε Approximation of Closed Contour Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . cn Abstract. Artiﬁcial neural network (ANN) is used in load forecasting widely. However, there are still some diﬃculties in choosing the input variables and selecting one appropriate architecture of the neural networks. According to the characteristics of electric short-term load forecasting, presents on a BPANN basing on rough set.