Download Advances in Neural Networks – ISNN 2012: 9th International by Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov PDF

By Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)

The two-volume set LNCS 7367 and 7368 constitutes the refereed court cases of the ninth overseas Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers awarded have been rigorously reviewed and chosen from quite a few submissions. The contributions are dependent in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development reputation; imaginative and prescient; picture processing; details processing; neurocontrol; and novel applications.

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Additional info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I

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L is called a Lipschitz constant. An FNN is trained using P known examples comprised of inputs xp and targets tp, both assumed to have all their components normalized over the interval [0,1]. Pruning Feedforward Neural Network Search Space Using Local Lipschitz Constants 13 Training attempts to minimize the sum of squared differences of the output computed by the FNN, op, with the target outputs, tp: F= 1 (t pk − o pk ) 2 .  2 p k Let F(w, X) be the criterion function of an FNN, w ∈ W, where W is the weight set (We assume W is compact), and X is the training set.

The proposed model is the extended architecture of conventional RBF NNs. The context values of context FCM are obtain through output space clustered by FCM. It helps reveal the relationships between the regions of the input space and the output space. -D. -K. -K. Kim space. The connection weight of proposed model is represented as three types of polynomial, unlike in most conventional RBFNN constructed with constant as connection weight. Weighted Least Square Estimation (WLSE) is used to estimate the coefficients of polynomial.

3 Application Study In this section, the models based on MI-KLM for estimating the level of saccharose of an orange juice from its observed near-infrared spectrum are compared. be, in which the data for learning and test are 150*700 and 68*700 respectively. The training data is shown in Fig. 1. -J. Zhao, J. Tang, and T. 66GHZ CPU with 768 RAM. Popular Gaussian kernel function are used to construct the final models based on ELM. 9 package [5]. The results are shown in Fig. 2. Fig. 1. Near-infrared spectrum Fig.

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