Quantum Computing Optimizer

Quantum Computing Optimizer (QCOptimizer) is an implementation of Step 2 of our information design technology. It is CAE tool for modelling and optimization of complex control systems which use multiple knowledge bases and quantum generalization. — User Manual

QCOptimizer main features are:

Free control system structure, model of control system can be constructed from various blocks (including but not limited to fuzzyfication, fuzzy knowledge bases, quantum generalization).
Optimization support:
- Genetic optimization:
  - Classical single-fitness GA
  - NSGA — multiple-fitness function optimization
  - Binary chromosomes
  - Real-valued chromosomes
- Non-genetical algorithms:
  - Gradient descent
  - Simplex descent method
- Fitness-function calculation:
  - Based on learning signal files
  - Based on external signal
  - Calculated in external program (typically Matlab/Simulink)
- Optimization target selection: can select any subset of available sybsystems for optimization
- Optimization speed-up options:
  - Parallel execution of several optimization algorithms, operating over different parts of model
  - Optional duplicate chromosome detection
Import of SCOptimizer knowledge bases
Support data exchange link with external programs

Screenshorts

Main program window

Main program window with model of complex control system inluding three fuzzy controllers and one quantum generalization module.

Quantum generalization module parameters

Dialog window used to set parameters of quantum generalization block.

Fuzzy knowledge base module parameters

Dialog window used to set parameters of fuzzy inference knowledge base. Table representation is shown, graphical representation is also avialable.

Fuzzyfication module parameters

Dialog window used to set parameters of fuzzification block. Membership functions can be edited either by entereing parameters in numerical form or dragging active points on graph by the mouse.