Neural classification of finite elements |
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| Affiliation: | 1. Holon Institute of Technology, P.O. Box 305, 52 Golomb Street, Holon 58102, Israel;2. Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Kyoto 611, Japan;1. School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, India;2. School of Mechanical Engineering, Lovely Professional University, Jalandhara, Panjab, 144411, India;1. School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China;2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China;3. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China;4. School of Technology, Nanjing Audit University, Nanjing, 211815, China;5. Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology), Nanjing, 210094, China;1. Advacned Forming Research Centre, Strathclyde Uiversity, Glasgow, UK;2. Mechanical Engineering Department, Faculty of Engineering, Helwan University, Egypt;3. Impression Technolgies Ltd, Covernty, UK;1. Department of Civil Engineering. University of Alicante, 03690 San Vicente del Raspeig, Spain;2. Department Applied Mathematics. University of Alicante. 03690 San Vicente del Raspeig, Spain;3. Department of Computer Science and Artificial Intelligence. University of Alicante. 03690 San Vicente del Raspeig, Spain;1. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India;2. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India |
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| Abstract: | The work deals with a comparative performance of finite elements, making use of their formulation as vectors (or patterns) in a multi-dimensional space of proper attributes. Since the attributes control the performance, elements defined by similar patterns and related to the same class should show similar behavior. The pattern classification may be carried out with the help a self-organizing feature map of Kohonen with the patterns corresponding to the input space. These networks learn both the distribution and topology of a set of input space. At the end of the learning process, the neurons become selectively tuned to classes of input patterns, thus specifying “family relationships” among the elements. The work makes use of the four attributes: the element dimensionality, its number of nodes, maximum degree of interpolation polynomials and number of degrees of freedom per node, though a more general characterization is also possible. |
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