By Nan Jiang, Yixian Yang, Xiaomin Ma, Zhaozhi Zhang (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)
This booklet is a part of a 3 quantity set that constitutes the refereed complaints 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 geared up in topical sections on neural fuzzy keep an eye on, neural networks for keep an eye on purposes, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear structures 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 buildings, neural networks for development popularity, SOMs, ICA/PCA, biomedical functions, feedforward neural networks, recurrent neural networks, neural networks for optimization, help vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and purposes 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
The number of the hidden layer is 10. The structure of this neural network is shown in Fig 1 (b). Model 2: A neural network with four layers. In generally, if the neurons in the hidden layer are sufficient enough, then the network can approximate an arbitrary nonlinear function. However, considering the complex nonlinear data come from the TCM clinic practice, we choose 4 layers neural network aiming to get a better convergence Study on Relationship Between NIHSS and TCM-SSASD g ( neti ) i j 1 wij 1 2 2 wmi g (netm ) m 1 wkm g (netk ) 2 k 1 3 2 4 3 5 6 21 19 59 20 60 Fig.
3. Scheme 2: Feature multiple models estimation. From above, it is shown that all apoplectics can be divided into different groups with different feature. Such as: from the diagnose point of view, there are ischemic apoplexy suffers and cerebral hemorrhage suffers; for the curing time, there are suffers cured within 0-1 day, 2-3 days, 6-8 days and 12-16 days; from the sex point of view, there is female and male. These data with distinct features cover all the field of the data sets. Therefore, we can build a independent feature model for every kinds of suffers groups with different features.
Zhang et al. their advantage? These problems become the focus topic of all Chinese medicine researchers. With the development of the modern methodology and the numerical TCM, the technology of data storage and data mining become impossible. Therefore, the exploring for the relationship between NIHSS and TCM-SSASD is not only important in the theory researches but also have a wise application foreground. BP Back Propagation neural network is a classic multi-level forward neural network. It is welcome by many researchers in the intelligent control field for its simple principle and distinguished approximation function to the nonlinear system.