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Quaternion neural network matlab program thesis

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A family of statistical viewing algorithms aspired by biological neural networks which are used to estimate tasks carried on large number of inputs that are generally unknown in Artificial Neural Networks. To develop intelligent systems neural advances have been carried out by the aspiration of biological neural networks. TO solve problems in prediction, control, associative memory, optimization and pattern reorganization, learning ability, distributed representation and computation, generalization ability, adaptively, fault tolerance, inherent contextual information processing and low energy consumption. To assign an input pattern which is represented by a feature vector to one pre specified class is pattern classification. Generating pairs form unknown functions I.
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How to apply neural networks for multiple experimental data

Quaternion neural network matlab program thesis
Quaternion neural network matlab program thesis
Quaternion neural network matlab program thesis
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Nonlinear Multidimensional Adaptive Modeling, System Identification and Prediction — Especially complex-valued and hyper-complex neural networks for natural phenomena, such as in modeling of environmental signals, complex industrial plants and trajectory tracking. Wind Modeling and Prediction — Using advanced nonlinear multivariate predictors. Simultaneous modeling of all the components of multidimensional and multivariate signals. One- and multi-dimensional adaptive denoising — Using adaptive signal processing algorithms and wavelets, with applications to biomedical signals, such as fMRI and medical images. Blind Source Separation and Extraction — Especially on-line adaptive algorithms for Electroencephalogram, MEG, and fMRI, especially based on some fundamental properties of signals, such as their nonlinear, stochastic or nonstationary nature.
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