By Dazhong Ma, Jinhai Liu, Zhanshan Wang (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 foreign Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers offered have been rigorously reviewed and chosen from various submissions. The contributions are dependent in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend reputation; imaginative and prescient; snapshot processing; info processing; neurocontrol; and novel applications.
Read Online or Download Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II PDF
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Extra info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II
Figure 1 has shown the representation of a chromosome, in which F represents a measure function and w1 , w2 , . . , wn represent the weights for the n features. Two encoding modes are employed in the chromosomes for considering the unit eﬀectiveness. The real value weight is used for the functions which are aﬀected by unit and the binary weight is used for the remaining functions. For measure function, EuM, MtM, ChM, DSC, and ReC are respectively selected when the value of F is in the interval [0, 20), [20, 40), [40, 60), [60, 80), and [80, 100) in the real value weight system; and MhM, CaM, and ESC are respectively selected when the value of F is ’01’, ’10’ or ’11’ in the binary weight system.
Wang, S. He, and H. 2 Experimental Results and Analyses Experiments on Artificial Data Set. In general, EuM is used in KNN. Here we carefully constructed an artiﬁcial data set which was based on the characteristics of the EuM and DSC. We chose these two types of metric because they are frequently used and easy to understand. The generated data set has two classes and how to deﬁne the feature attributes is shown in Table 1. The assumption for generating this artiﬁcial data set is that the features f1 , f2 are equally signiﬁcant for the classiﬁcation, and the features f3 and f4 are irrelevant features.
108×9 , and for the testing period. All the data were Multi-class Classification with One-Against-One Using Probabilistic Extreme Learning 15 Table 1. 2 Multi-class Classification In the study, three states are considered. Class ω1 denotes low load state, ω2 normal state and ω3 overload state. The ELM model of the binary classes was calculated following Eqs. (1)-(5). The number of hidden nodes L was 85. Parameters of probability density function were estimated by nonlinear least squares. Fig. 2 to Fig.