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Chun-Xiang Li, Dong-Xiao Niu, Li-Min Meng (auth.), Fuchun's Advances in Neural Networks - ISNN 2008: 5th International PDF

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

ISBN-10: 3540877339

ISBN-13: 9783540877332

ISBN-10: 3540877347

ISBN-13: 9783540877349

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

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

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Additional 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

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 .

Keywords: Load forecasting, Rough set, Artificial neural network, BP ANN. 1 Introduction Short-term load forecasting plays an important role in power system planning and operation. It is very important in enhancing the operating efficiency of distribution network, improving the quality power of supply and so on. Precise forecasting benefits enhancing the security and stability of the power system, and reduce the cost of the electricity generation. Therefore, many traditional forecasting models have been proposed and implemented in this field [1],[2], such as multiple linear regression, general exponential smoothing, stochastic process, auto-regressive moving-average model and so on.

An Approach Based on Neural Networks for Estimation and Generalization of Crossflow Filtration Processes. Applied Soft Computing 8, 590–598 (2008) 14. : Fuzzy Short-term Electric Load Forecasting Using Kalman Filter. IEEE Proc. Gener. Transm. Distrib. 153, 217–227 (2006) 15. : Combined Optimum Gray Neural Network Model of The Seasonal Power Load Forecasting With the Double Trends. Proceeding of the CSEE 22, 29–32 (2002) 16. : Rough sets. International Journal of Computer InformationScience 5, 341–356 (1982) 17.

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Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008, Proceedings, Part II by Chun-Xiang Li, Dong-Xiao Niu, Li-Min Meng (auth.), Fuchun Sun, Jianwei Zhang, Ying Tan, Jinde Cao, Wen Yu (eds.)


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