Statements (73)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:philosopher
|
| gptkbp:bfsLayer |
5
|
| gptkbp:bfsParent |
gptkb:Mc_Culloch-Pitts_neuron
|
| gptkbp:adapted_into |
various applications
different domains |
| gptkbp:applies_to |
gptkb:robot
signal processing |
| gptkbp:can_be |
decision boundaries
|
| gptkbp:can_be_used_with |
fuzzy systems
other algorithms other logic units |
| gptkbp:designed_by |
gptkb:John_von_Neumann
|
| gptkbp:emulation |
software models
|
| gptkbp:has_method |
binary values
|
| https://www.w3.org/2000/01/rdf-schema#label |
threshold logic unit
|
| gptkbp:input_output |
binary values
|
| gptkbp:is_analyzed_in |
statistical methods
|
| gptkbp:is_atype_of |
non-linear function
|
| gptkbp:is_characterized_by |
decision making
binary output activation threshold input-output mapping |
| gptkbp:is_evaluated_by |
performance metrics
real-time systems real-world scenarios simulation environments input signals threshold criteria |
| gptkbp:is_fundamental_to |
gptkb:software_framework
|
| gptkbp:is_implemented_in |
gptkb:mobile_application
embedded systems Boolean functions FPGA technology hardware circuits |
| gptkbp:is_influenced_by |
learning rate
activation functions input weights |
| gptkbp:is_integrated_with |
other computational units
|
| gptkbp:is_optimized_for |
gptkb:benchmark
|
| gptkbp:is_part_of |
gptkb:Artificial_Intelligence
intelligent systems computational models cognitive computing neural computation |
| gptkbp:is_related_to |
data analysis
fuzzy logic digital circuits signal detection |
| gptkbp:is_represented_in |
gptkb:Footballer
|
| gptkbp:is_similar_to |
gptkb:microprocessor
|
| gptkbp:is_subject_to |
noise
|
| gptkbp:is_used_for |
data classification
enhance performance pattern matching feature extraction model complex systems solve problems |
| gptkbp:is_used_in |
gptkb:military_base
image processing automated systems forecasting models decision support systems pattern recognition |
| gptkbp:is_utilized_in |
classification tasks
data mining optimization problems |
| gptkbp:performed_by |
thresholding operation
|
| gptkbp:related_to |
gptkb:microprocessor
|
| gptkbp:setting |
specific tasks
|
| gptkbp:training |
gradient descent
learning algorithms training datasets |
| gptkbp:used_in |
gptkb:microprocessor
|