By Nan Jiang, Yixian Yang, Xiaomin Ma, Zhaozhi Zhang (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)
This e-book is a part of a 3 quantity set that constitutes the refereed court cases of the 4th overseas Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007.
The 262 revised lengthy papers and 192 revised brief papers offered have been conscientiously reviewed and chosen from a complete of 1,975 submissions. The papers are equipped in topical sections on neural fuzzy regulate, neural networks for keep watch over purposes, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear platforms modeling, robotics, balance research of neural networks, studying and approximation, info mining and have extraction, chaos and synchronization, neural fuzzy platforms, education and studying algorithms for neural networks, neural community constructions, neural networks for development attractiveness, SOMs, ICA/PCA, biomedical purposes, feedforward neural networks, recurrent neural networks, neural networks for optimization, aid vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and functions of neural networks.
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Additional resources for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part III
3. Scheme of average estimation by multiple models where X is the overall data set, x i is the element of the ith feature subset Ωi . For every subset Ωi , k models named f i (x i ) are built. In the diagnose process, if the suffer x ∈ Ω j , then the j th neural network model f j (x i ) is adopted to diagnose. The principle figure is as Fig. 4. Feature Classification Neural Network 1 Value of NIHSS Neural Network 2 Value of NIHSS Neural Network n-1 Value of NIHSS Neural Network n Value of NIHSS Feature set 1 Database of the TCM Standardized Sheet Feature set 2 Feature set n-1 Feature set n Fig.
The Heavy Power Lab in St. John’s University installed a set of precious instrument (Hipotronics DDX-7000 Digital Discharge Detector). Therefore, the school has the capability of doing insulation diagnosis related researches. Power transformer is one of the most important apparatuses in a power delivery system. Breakdown of a power transformer can cause an interruption in electricity supply and result in a loss of considerable profits . Therefore, detecting insulation defects in a power transformer as early as possible is of priority concern to a power transformer user.
C) High-voltage corona discharge (Type C). (d) Healthy transformer (Type D). Fig. 6. ): Typical PD patterns of four defect types From the literature survey, several models and learning algorithms of ANN have been proposed for solving the patterns classification problems . In this paper, we establish a triple-layer feed-forward BPNN, as shown in Fig. 7, for solving the PD patterns classification problem. The number of neurons in the output layer is set at the number of defect types. The input data for the BPNN is the field measuring 3-D PD pattern.