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Second, pattern element identification is modelled as a supervised classification problem and the deep neural network technique is applied for the accurate classification of pattern elements.
In the past few years, the dynamic behaviors of coupled interacting loops neural networks have been widely studied due to their extensive applications in classification of pattern recognition, signal processing, image processing, engineering optimization and animal locomotion, and other areas, see the references therein.
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Fingerprint identification, or the science of dactyloscopy, relies on the analysis and classification of patterns observed in individual prints.
Fig. 3 a f Classification of patterns of the time coefficients of EOF1 using cluster analysis.
Finally, the classification of patterns has been achieved by using adaptive neuro-fuzzy techniques.
Cluster analysis is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters).
A complete classification of patterns of spectra for ion diffusion and trapping (complex impedance and complex capacitance) is provided, in terms of characteristic frequencies that depend on the steady state.
Since Chua and Yang [1, 2] introduced a cellular neural network in 1988, it has received great attention because of its various applications such as classification of patterns, associative memories and optimization, etc.
Firstly, the emergent computational properties in CA can in an important sense be objectively defined (see Crutchfield 1994a and the more accessible Crutchfield 1994b): although it is customary in this setting to talk of emergent computation being "in the eye of the beholder", the detection and classification of patterns is itself algorithmic, not just phenomenological.
In recent years, considerable attention has been paid to bidirectional associative memory (BAM) Cohen-Grossberg neural networks [1] due to their potential applications in various fields such as neural biology, pattern recognition, classification of patterns, parallel computation and so on [2 4].
Five motivating problems: constructibility in geometry, classification of patterns in two dimensions, error-correcting codes, cryptography, and the analysis of symmetry in structures.
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