optimizeDesign

Optimize circuit design in simulation environment

Since R2024b

Syntax

[sol,metric] = obj.optimizeDesign

[sol,metric] = optimizeDesign(obj,NumParallelSims=8,MaxNumberSims=100)

Description

[sol,metric] = obj.optimizeDesign optimizes the circuit parameters of the msbOptimizer object obj.

[sol,metric] = optimizeDesign(obj,NumParallelSims=8,MaxNumberSims=100) optimizes the circuit parameters by running 8 simulations per iteration and limits the maximum number of simulations to 100.

Examples

Optimize Parameters for Second-Order DSM in Simulink

Define the performance specifications of a second-order DSM.

outputTable=table();
outputTable.Test=["ACMeas";"ACMeas";"ACMeas";"ACMeas";"ACMeas"];
outputTable.Name={'SNR';'SFDR';'SINAD';'ENOB';'NoiseFloor'};
outputTable.Units={'dB';'dB';'dB';'bits';'dB'};
outputTable.Spec={'> 72';'> 74';'> 72';'maximize 11.5';'< -78'}

outputTable=5×4 table
      Test           Name          Units            Spec       
    ________    ______________    ________    _________________

    "ACMeas"    {'SNR'       }    {'dB'  }    {'> 72'         }
    "ACMeas"    {'SFDR'      }    {'dB'  }    {'> 74'         }
    "ACMeas"    {'SINAD'     }    {'dB'  }    {'> 72'         }
    "ACMeas"    {'ENOB'      }    {'bits'}    {'maximize 11.5'}
    "ACMeas"    {'NoiseFloor'}    {'dB'  }    {'< -78'        }

Define the variables to optimize.

variableTable=table();
variableTable.parameters={'a1';'a2';'b1';'b2'};
variableTable.values=["0.15:0.005:0.16";"0.55:0.005:0.7";"0.15:0.005:0.16";"0.55:0.005:0.7"]

variableTable=4×2 table
    parameters         values      
    __________    _________________

      {'a1'}      "0.15:0.005:0.16"
      {'a2'}      "0.55:0.005:0.7" 
      {'b1'}      "0.15:0.005:0.16"
      {'b2'}      "0.55:0.005:0.7" 

Create the msbOptimizer object.

moptimizer = msbOptimizer(SimulationEnvironment='simulink',OutputsSetup=outputTable,VariableSetup=variableTable,DesignName='DSM2ndOrder')

moptimizer = 
  msbOptimizer with properties:

               DesignName: 'DSM2ndOrder'
                   Solver: "surrogateopt"
    SimulationEnvironment: 'simulink'
             BestSolution: []
              BestMetrics: []
     FinalOptimizerStatus: []
             OutputsSetup: [5×4 table]
          ParametersSetup: [4×2 table]
           ParameterNames: ["a1"    "a2"    "b1"    "b2"]
          ParameterValues: ["0.15:0.005:0.16"    "0.55:0.005:0.7"    "0.15:0.005:0.16"    "0.55:0.005:0.7"]
                    Eflag: []
                   Trials: []
              Constraints: [5×9 table]
                  Corners: []

Optimize the parameters.

[sol,metric] = moptimizer.optimizeDesign

Maximum number of simulations: 100
Number of parallel simulations: 1

Optimizer was able to meet all the specifications.

sol=4×2 table
    Name    Value
    ____    _____

    "a1"     0.15
    "a2"    0.575
    "b1"    0.155
    "b2"    0.635

metric=5×4 table
        Name        FinalMetrics         Specs         Units 
    ____________    ____________    _______________    ______

    "SNR"              72.519       "> 72"             "dB"  
    "SFDR"             80.869       "> 74"             "dB"  
    "SINAD"            72.519       "> 72"             "dB"  
    "ENOB"             11.754       "maximize 11.5"    "bits"
    "NoiseFloor"      -80.755       "< -78"            "dB"  

As you can see, the function meets the required specifications. You can use the check point file to continue further optimization from the current state.

Input Arguments

obj — Circuit design to optimize

msbOptimizer object

Circuit design to optimize, specified as an msbOptimizer object The object includes the name of the simulation environment, the name of the circuit design, the performance specifications, and the variables to optimize.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: [sol,metric] =obj.optimizeDesign(ProgressPlot='true') optimizes the circuit design defined in the obj object and shows the optimization progress in a plot.

NumParallelSims — Number of parallel simulations per iteration

1 (default) | nonnegative integer scalar

Number of parallel simulations per iteration, specified as a nonnegative integer scalar.

Data Types: double

MaxNumberSims — Maximum number of simulations to run

max(100,10*nvar) (default) | nonnegative integer scalar

Maximum number of simulations to run, specified as a nonnegative integer scalar. The default is max(100,10*nvar), where nvar is the number of problem variables.

Data Types: double

MinSurrogatePoints — Minimum number of random sample points to create at the start of a surrogate creation phase

max(20,2*nvar) (default) | nonnegative integer scalar

Minimum number of random sample points to create at the start of a surrogate creation phase, specified as a nonnegative integer scalar. The default is max(20,2*nvar), where nvar is the number of problem variables. For more information, see .Surrogate Optimization Algorithm (Global Optimization Toolbox).

Data Types: double

ProgressPlot — Show optimization progress

true (default) | false

Show optimization progress in a plot.

Data Types: logical

MetricPlot — Metric name to plot on progress plot

string | character vector

The metric name to plot on the progress plot, specified as a string or character vector. The default is the first metric on the constraint table.

CheckPointFileName — Name of file to create checkpoints and restart optimization

myCheckPoint (default) | string | character vector

Name of the file to create checkpoints and restart the optimization process, specified as a string or character vector.

UseCheckPoint — Use checkpoint file for optimization

true (default) | false

Use the checkpoint file for optimization. The function can only use this argument after the creation of a checkpoint file at the end of a simulation run.

Data Types: logical

solver — Optimization solver

surrogateopt (default)

Optimization solver used to optimize circuit parameters. You can only select surrogateopt.

ObjectiveFunction — Handle to objective function

object handle

Handle to objective function that evaluates the objective.

InitialPoints — Initial points for surrogate optimization

[] (default) | matrix | struct

Initial points for surrogate optimization, specified as a matrix or struct.

Data Types: double

RngSeed — Random number generator seed

1 (default) | nonnegative integer scalar

Random number generator seed, specified as a nonnegative integer scalar.

Data Types: double

RngAlgorithm — Random number generator algorithm used by optimization solver

twister (default) | string

Random number generator algorithm used by optimization solver, specified as a string.

Data Types: char

Output Arguments

sol — Optimized variables or parameters

table

Optimized variables or parameters, returned as a table. The elements of the table matches the variables or parameters defined in the VariableSetup argument in mbsOptimizer object.

metric — Performance metrics using optimized variables or parameters

table

Performance metrics using optimized variables or parameters, returned as a table. The elements of the table matches the variables or parameters defined in the OutputsSetup argument in mbsOptimizer object.

Version History

Introduced in R2024b