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FuzzyEdgeDetection
- on Neuro Fuzzy edge detection
nnw
- Neuro with Delphi-exempl
DTC-neurofuzzy
- DIRECT TORQUE NEURO FUZZY SPEED CONTROL OF AN INDUCTION MACHINE
25-674
- neuro fuzzy classification
generate_two_moons
- generate two half moon Radial basis function neuro networks
nfc
- neuro-fuzzy classify
Neuro
- NeuroVCL V1.1 components for Borland Delphi 5
power_acdrive
- The induction motor is fed by a current-controlled PWM inverter which is built using a Universal Bridge block. The motor drives a mechanical load characterized by inertia J, friction coeficient B, and load torque TL . The speed control loop uses a pr
A-neuro-fuzzy-system-for-looper-tension-control-i
- A looper tension control system is common to many rolling processes.Con ventional tension controllers for mill actuation systems are based on a rolling model.The y therefore cannot deal effectively with unmodeled dynamics and large parameter variat
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific