Emerging Intelligent Computing Technology and Applications: by Chunhua Feng, Zhenkun Huang (auth.), De-Shuang Huang,

By Chunhua Feng, Zhenkun Huang (auth.), De-Shuang Huang, Phalguni Gupta, Xiang Zhang, Prashan Premaratne (eds.)

This e-book constitutes the refereed court cases of the eighth foreign convention on clever Computing, ICIC 2012, held in Huangshan, China, in July 2012. The 242 revised complete papers awarded within the 3 volumes LNCS 7389, LNAI 7390, and CCIS 304 have been conscientiously reviewed and chosen from 753 submissions. The papers during this quantity (CCIS 304) are prepared in topical sections on Neural Networks; Particle Swarm Optimization and area of interest expertise; Kernel equipment and aiding Vector Machines; Biology encouraged Computing and Optimization; wisdom Discovery and information Mining; clever Computing in Bioinformatics; clever Computing in development popularity; clever Computing in picture Processing; clever Computing in machine imaginative and prescient; clever regulate and Automation; wisdom Representation/Reasoning and professional structures; Advances in info defense; Protein and Gene Bioinformatics; tender Computing and Bio-Inspired Techiques in Real-World functions; Bio-Inspired Computing and Applications.

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Additional info for Emerging Intelligent Computing Technology and Applications: 8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012. Proceedings

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Table 2. Prediction error (MSE) of different models Evaluation/Model E1 (original data) E1 (difference data) E2 (original data) E2(difference data) RBMs (proposed) 1622 1215 1490 979 MLP ARIMA (with PSO) 2655 1330 1317 1258 2694 1348 1137 1254 The best of IJCNN’04 408 222 - The worst of IJCNN’04 1156 1229 - 22 4 T. Kuremoto et al. Conclusion A novel neural network model for time series forecasting was proposed. The proposed model is a kind of deep believe network (DBN) composed by multiple restricted Boltzmann machines (RBMs).

30–37, 2012. © Springer-Verlag Berlin Heidelberg 2012 Precipitation Control for Mixed solution Based on Fuzzy Adaptive Robust Algorithm 31 In recent years, Fuzzy control has become very popular and enjoys a wide variety of application areas [9-12]. However, the tracking errors of most fuzzy control systems can be merely converged to a global field of residual errors, the global field would augment along with system uncertainty [13], so fuzzy control is not robust for system uncertainty. In paper [14], an effectual fuzzy adaptive controller was contrived for a class of nth-order single-input single-output (SISO) nonlinear systems, but the calculation quantum is too great.

In Section 2, we will introduce the theoretical background of MRNN. Then we will derive the learning rules of MRNN in Section 3. -S. Huang et al. ): ICIC 2012, CCIS 304, pp. 10–16, 2012. © Springer-Verlag Berlin Heidelberg 2012 Minimum Risk Neural Networks and Weight Decay Technique 11 We will demonstrate the performance of MRNN with several real application examples in the UCI databases in Section 4. Finally, in Section 5 we will make a summary of the testing results in the entire study. 1 Theoretical Background Multi-layer Perceptrons with Weight Decay (MLPWD) Although minimizing error function enables the neural network to build precise nonlinear model fitting to the training examples, that is, the model possesses repetition.

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