n1 = 4 | n2 =3 | ARRAY aa[n1], bb[n2] data 50, 20, 30, 40; 27, 0, 100 | read aa, bb in VS .NET

Encode QR Code in VS .NET n1 = 4 | n2 =3 | ARRAY aa[n1], bb[n2] data 50, 20, 30, 40; 27, 0, 100 | read aa, bb
n1 = 4 | n2 =3 | ARRAY aa[n1], bb[n2] data 50, 20, 30, 40; 27, 0, 100 | read aa, bb
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We now declare and fill the desired n-dimensional parameter arrays a and b with
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n = n1 * n2 | ARRAY a[n],b[n] for k = 1 to n2 for i = 1 to n1 a[i + (k - 1) * n1] = aa[i] | b[i + (k - 1) * n1] = bb[k] next next
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The [i + (k 1) * n1] th model has the parameter combination aa[i], bb[k] The smaller arrays aa and bb are much easier to read, write, and store than
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Parameter-influence Studies and Vectorization
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the repetitious a and b arrays This procedure can be extended to three or more parameters 4-3 Programming Parameter-influence Studies
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(a) Introduction
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Before vectorizing a model, we usually check it out in scalar form This also simplifies sorting defined-variable assignments (Sections 1-9 and 2-1) When the simulation works, one will want to program output procedures specifically designed to evaluate the effects of changing parameters Our toy example was simple enough But real parameter-influence studies involve multiple parameters and possibly very many parameter combinations We must vary
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the design parameters that we want to optimize under different conditions, parameters that represent these different conditions (eg, different tem-
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peratures and different initial conditions) As we noted in Section 1-17, simulations quickly produce large volumes of time-history graphs and numerical tables Meaningful evaluation of such results is a very real problem DESIRE experiment-protocol commands can list successive parameter settings and simulation results even entire arrays in a journal file2 This preserves the data, but may not relate successive results in a meaningful way A better plan is to let experiment-protocol scripts or DYNAMIC program segments write data tables directly into space-delimited text files These can then be fed to standard spreadsheet and relational-database programs for analysis (data mining), presentations, and storage DESIRE experiment-protocol scripts can readily access external programs and exchange file data with them
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(b) Measures of System Effectiveness
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A system combines hardware, people, and/or modes of operation for some purpose Then, the very definition of an engineering system requires that quantitative measures of its effectiveness be defined These measures are normally numerical functions of system parameters We often use cost-related functionals such as integrals of system-variable time histories, such as the control-system error measures in Section 1-14 Parameter-influence studies must define effectiveness measures and compute their values for each parameter combination
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Refer to the DESIRE reference manual in the book CD
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Parameter-influence Studies, Model Replication, and Monte Carlo Simulation
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We shall want to maximize measures of system effectiveness (or minimize cost measures) as functions of system parameters More often than not, however, practical design is not the result of straightforward global optimization but involves compromises:
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Conflicting measures (say cost and performance) may need individual
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consideration a single measure (eg, performance per unit cost) may not do One may have to compromise between performance results obtained under different conditions (eg, different signal amplitudes, temperatures, or initial conditions) Simulation results are only raw material for making such decisions The user has to make intelligent compromises
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(c) Crossplotting Results
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Consider a model producing a performance measure such as the controlsystem integrated squared error ISE in Section 1-14,
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d/dt ISE = (x u)2 (ISE, integral squared error)
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(4-9)
Its value ISE = ISE(t0 + TMAX) at the end of a simulation run can be a useful control-system performance measure To see clearly how ISE depends on a system parameter, say the servo damping coefficient r, we may want to plot a graph of ISE versus r 1 An n-run repeated-run study (Section 4-2a) makes n simulation runs with n parameter values of r = r0, r0 + DELr, r0 + 2 DELr, and produces a corresponding ISE value [Eq (4-9)] at the end of each run If a runtime time-history display is not needed, the experiment-protocol script can crossplot ISE versus r as successive runs proceed:
for i = 1 to n r = r + (i 1) * DELr drun plot r, ISE, c | -- (c = 1, 2, is a graph color) reset next
Instead, one may want to watch a runtime time-history display and save corresponding values of r and ISE in two n-dimensional arrays declared with
ARRAY rr[n], ise[n]