FIELD: neuro-computer engineering, possible use for classification of number classes by module p.
SUBSTANCE: neuron network contains input layer, concealed layer, to which input layer is connected recursively in couples, and an output layer.
EFFECT: expanded capabilities of known neuron network for classification of residuals.
2 dwg
Title | Year | Author | Number |
---|---|---|---|
FINITE RING NEURAL NETWORK | 2018 |
|
RU2701064C1 |
FINITE RING NEURAL NETWORK | 2020 |
|
RU2759964C1 |
NEURAL NETWORK WITH THRESHOLD (k, t) STRUCTURE FOR RESIDUAL CODE - BINARY POSITION CODE CONVERSION | 2008 |
|
RU2380751C1 |
CONVEYOR NEURON NETWORK OF FINITE RING | 2006 |
|
RU2317584C1 |
NEURON NETWORK FOR BROADENING TUPLE OF NUMERIC SUBTRACTIONS SYSTEM | 2003 |
|
RU2256226C2 |
NEURON NETWORK FOR FINDING, LOCALIZING AND CORRECTING ERRORS IN RESIDUAL CLASSES SYSTEM | 2005 |
|
RU2301442C2 |
NEURON NETWORK FOR TRANSFORMATION OF RESIDUAL CODE TO BINARY POSITIONAL CODE | 2006 |
|
RU2318238C1 |
NEURON NETWORK FOR CONVERSION OF POLYADIC CODE TO REMAINDER CLASSES SYSTEM CODE | 2003 |
|
RU2258257C2 |
ADAPTIVE PARALLEL-CONVEYOR NEUTRON NETWORK FOR CORRECTION OF ERRORS | 2003 |
|
RU2279131C2 |
NEURON NETWORK FOR DIVIDING NUMBERS WHICH ARE REPRESENTED IN A SYSTEM OF RESIDUAL CLASSES | 2006 |
|
RU2318239C1 |
Authors
Dates
2006-06-27—Published
2003-08-07—Filed