Volterra Neural Network and Method
AssigneeNorth Carolina State University
InventorsHamid Krim, Siddharth Roheda, Sally Ghanem
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Abstract
An exemplified methods and systems provides a Volterra filter network architecture that employs a cascaded implementation and a plurality of kernels, a set of which is configured to execute an nth order filter, wherein the plurality of kernels of the nth order filters are repeatedly configured in a plurality of cascading layers of interconnected kernels to form a cascading hierarchical structure that approximates a high-order filter.
Claims22
A computer-implemented neural network system comprising a processor and a memory configured to execute a memory structure comprising nodes connected via a plurality of layers, the memory structure comprising:
- a plurality of kernels, a set of which being configured to execute an nth order Volterra filter, wherein the plurality of kernels of the nth order Volterra filters are repeatedly configured in a plurality of cascading layers of interconnected kernels to form a cascading hierarchical structure that approximates a high-order Volterra filter.
A method comprising:
- training, via a computing system, a Volterra neural network comprising plurality of kernels each configured to execute an nth order filter, wherein the plurality of kernels of the nth order filters are repeatedly configured in a plurality of cascading layers of interconnected kernels to form a cascading hierarchical structure that approximates a high-order filter substantially greater than n;
- and
- using the trained neural network in a video or image processing application.
A non-transitory computer readable medium comprising instructions, wherein execution of the instructions, by a processor, cause the processor to:
- train a Volterra neural network comprising plurality of kernels each configured to execute an nth order filter, wherein the plurality of kernels of the nth order filters are repeatedly configured in a plurality of cascading layers of interconnected kernels to form a cascading hierarchical structure that approximates a high-order filter substantially greater than n;
- and and
- use the trained neural network in a video or image processing application.
Dependencies are inferred from the claim text.