By Daqi Zhu, Qingbin Sang (auth.), De-Shuang Huang, Kang Li, George William Irwin (eds.)
The foreign convention on clever Computing (ICIC) used to be shaped to supply an annual discussion board with commitment to the rising and not easy issues in man made intelligence, computing device studying, bioinformatics, and computational biology, and so forth. It goals to compile researchers and practitioners from either the academia and to percentage rules, difficulties and options on the topic of the multifaceted points of clever computing. The ICIC 2006 to be held in Kunming, Yunnan, China, 16-19 August 2006 is the second one overseas convention on clever Computing, that is equipped upon the luck of ICIC 2005 held in Hefei, China, 2005. This 12 months, the convention normally concentrates at the theories & methodologies in addition to the rising purposes of clever computing. It intends to unify the modern clever computing innovations inside an vital framework that highlights the tendencies in complex computational intelligence and bridges the theoretical study with the purposes. particularly, the bio-inspired computing emerges as a key function in pursuing for novel expertise in lately years. The ensuing suggestions vitalize the existence technology engineering and lifestyle functions. In mild of this pattern, the subject for this convention is the rising clever Computing know-how and functions. Papers with regards to this subject matter have been particularly solicited, together with theories, methodologies, and functions in technological know-how and technology.
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Extra resources for Intelligent Computing: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006. Proceedings, Part I
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Mastronardi, and M. Marinelli • one input neuron represented by the voxel’s CT Number; • n output neurons representing the number of ROIs to extract from the original image. The map is one-dimensional and the neurons are set on a virtual line, so the distance between two consecutive neurons is unitary. In the initialization step neurons’ weights are set to zero, for assuring the same association order to all slices. In the training procedure the weights will adapt to the CT Numbers of the presented slice.
S. Huang, K. W. ): ICIC 2006, LNCS 4113, pp. 32–41, 2006. c Springer-Verlag Berlin Heidelberg 2006 A Neural Network with Finite-Time Convergence 33 quadratic programming[8], minimax problems and so on. Thus it has many important applications(see [1,6,7,8] and the references therein). In many scientific and engineering applications, real-time on-line solutions of problem (1) are desired. However, traditional algorithms[1,6,8,11] are not suitable for a real-time on-line implementation on the computer.
R. : Neural Network Methods for Volumetric Magnetic Resonance Imaging of the Human Brain. IEEE, (1996) 1488–1496 6. Reddick, W. , Glass, J. , Cook, E. , Elkin, T. , Deaton, R. : Automated Segmentation and Classification of Multispectral Magnetic Resonance Images of Brain Using Artificial Neural Networks. IEEE Trans. Medical Imaging, 16(1997)911–918 7. L. : Fundamentals of Neural Networks – Architectures, Algorithms and Applications 8. : 3D Tomography: Computerized Modeling of 2D scanned images.