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GTM neurolike structures provide solutions of such tasks as: pattern recognition, prediction tasks, classification, Principal Components Analysis, factor analysis, optimization, lost data renewal or its (data) compression, realization of information security methods, solution of algebraic equations systems (including underdetermined and overdetermined), high dimensional data visualization, etc.
After the full understanding of principal component analysis and kernel principal component analysis, the validity of kernel principal component analysis method is proved, and a new grid-based parameter optimization algorithm is introduced. Combining with BPNN, the classification experimental results show that this parameter optimization algorithm can get good results, The performance is superior...
This paper describes a method to optimise sensing positions of a two-dimensional distributive tactile sensor for determining an applied load position from surface deflections. The distributive approach relies on coupling between sensing positions that capture a pattern of response to contacting load. The paper describes an experimental arrangement and the corresponding mathematical model that incorporates...
The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However,...
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