Neural Network Models for Learning Patterns and Pattern Sequences

Sachin, H. B. (2017) Neural Network Models for Learning Patterns and Pattern Sequences. Masters thesis, Indian Institute of Science Education and Research Kolkata.

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Neural networks are inspired from the biological neuronal networks which are modeled based on some properties of the neuron. These networks are capable of learning and having associative memory, which is of importance in pattern recognition, error correction, and time sequence retention to name a few. The associative memory property of one of the neural network, the hopfield network, has been studied and it is used to learn static patterns or memories. The problem of pattern sequence recognition has been studied using crosscorrelation matrix memory model. In both the cases the error correction capabilities and the memory of the network is studied through simulations.

Item Type: Thesis (Masters)
Additional Information: Supervisor: Dr. Anandamohan Ghosh
Uncontrolled Keywords: Neural Network; Learning Patterns; Pattern Recognition; Pattern Sequence; Pattern Sequence Recognition
Subjects: Q Science > QC Physics
Divisions: Department of Physical Sciences
Depositing User: IISER Kolkata Librarian
Date Deposited: 08 Nov 2018 05:33
Last Modified: 08 Nov 2018 05:33

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