Variables input

A Monte Carlo simulation is fast to perform on a computer, and the presentation of the results is attractive. However, one cannot guarantee that the outcome of a Monte Carlo simulation run twice with the same input variables will yield exactly the same output, making the result less auditable. The more simulation runs performed, the less of a problem this becomes. The simulation as described does not indicate which of the input variables the result is most sensitive to, but one of the routines in Crystal Ball and Risk does allow a sensitivity analysis to be performed as the simulation is run.This is done by calculating the correlation coefficient of each input variable with the outcome (for example between area and UR). The higher the coefficient, the stronger the dependence between the input variable and the outcome. 

The parametric method is an established statistical technique used for combining variables containing uncertainties, and has been advocated for use within the oil and gas industry as an alternative to Monte Carlo simulation. The main advantages of the method are its simplicity and its ability to identify the sensitivity of the result to the input variables. This allows a ranking of the variables in terms of their impact on the uncertainty of the result, and hence indicates where effort should be directed to better understand or manage the key variables in order to intervene to mitigate downside and/or take advantage of upside in the outcome. 

This short-cut method could be repeated to include another variable, and could therefore be an alternative to the previous two methods introduced. This method can always be used as a last resort, but beware that the range of uncertainty narrows each time the process is repeated because the tails of the Input variables are always neglected. This can lead to a false impression of the range of uncertainty in the final result. 

Given n input variables, x , the variable y ( q. (2) is modeled analogously to the case with one input variable. 

Multiple linear regression analysis is a widely used method, in this case assuming that a linear relationship exists between solubility and the 18 input variables. The multilinear regression analy.si.s was performed by the SPSS program [30]. The training set was used to build a model, and the test set was used for the prediction of solubility. The MLRA model provided, for the training set, a correlation coefficient r = 0.92 and a standard deviation of, s = 0,78, and for the test set, r = 0.94 and s = 0.68. 

The quality and yield of carbon black depends on the quaUty of the feedstock, reactor design, and input variables. The stmcture is controlled by the addition of alkaU metals to the reaction or mixing 2ones. Usual practice is to use aqueous solutions of alkaU metal salts such as potassium chloride or potassium hydroxide sprayed into the combustion chamber or added to the make oil in the oil injector. Alkaline-earth compounds such as calcium acetate that increase the specific surface area are introduced in a similar manner. 

In the context of chemometrics, optimization refers to the use of estimated parameters to control and optimize the outcome of experiments. Given a model that relates input variables to the output of a system, it is possible to find the set of inputs that optimizes the output. The system to be optimized may pertain to any type of analytical process, such as increasing resolution in hplc separations, increasing sensitivity in atomic emission spectrometry by controlling fuel and oxidant flow rates (14), or even in industrial processes, to optimize yield of a reaction as a function of input variables, temperature, pressure, and reactant concentration. The outputs ate the dependent variables, usually quantities such as instmment response, yield of a reaction, and resolution, and the input, or independent, variables are typically quantities like instmment settings, reaction conditions, or experimental media. 

The relationship between output variables, called the response, and the input variables is called the response function and is associated with a response surface. When the precise mathematical model of the response surface is not known, it is still possible to use sequential procedures to optimize the system. One of the most popular algorithms for this purpose is the simplex method and its many variations (63,64). 

To reflect this type of reasoning, a KBS captures quaHtative relationships between variables. By contrast, a conventional program that implements the flow equation calculates the value of the flow rate for numerical values of the input variables, ie, orifice diameter, orifice coefficient, and Hquid height. 

Nonlinear versus Linear Models If F, and k are constant, then Eq. (8-1) is an example of a linear differential equation model. In a linear equation, the output and input variables and their derivatives only appear to the first power. If the rate of reac tion were second order, then the resiilting dynamic mass balance woiild be ... 

Monte Carlo simulation is a numerical experimentation technique to obtain the statistics of the output variables of a function, given the statistics of the input variables. In each experiment or trial, the values of the input random variables are sampled based on their distributions, and the output variables are calculated using the computational model. The generation of a set of random numbers is central to the technique, which can then be used to generate a random variable from a given distribution. The simulation can only be performed using computers due to the large number of trials required. 

We can use Monte Carlo simulation to determine the mean and standard deviation of a function with knowledge of the mean and standard deviation of the input variables. Returning to the problem of the tensile stress distribution in the rectangular bar, the stress was given by ... 

The state of a system may be defined as The set of variables (called the state variables) which at some initial time Iq, together with the input variables completely determine the behaviour of the system for time t > to -... 

The size of the universes of diseourse will depend upon the expeeted range (usually up to the saturation level) of the input variables. Assume for the system about to be eonsidered that e has a range of 6 and ce a range of 1. 

Input variables specified for each error iUialy/Lil and how they were selected,... 

Viscosities of the siloxanes were predicted over a temperature range of 298-348 K. The semi-log plot of viscosity as a function of temperature was linear for the ring compounds. However, for the chain compounds, the viscosity increased rapidly with an increase in the chain length of the molecule. A simple 2-4-1 neural network architecture was used for the viscosity predictions. The molecular configuration was not considered here because of the direct positive effect of addition of both M and D groups on viscosity. The two input variables, therefore, were the siloxane type and the temperature level. Only one hidden layer with four nodes was used. The predicted variable was the viscosity of the siloxane. 

A very simple 2-4-1 neural network architecture with two input nodes, one hidden layer with four nodes, and one output node was used in each case. The two input variables were the number of methylene groups and the temperature. Although neural networks have the ability to learn all the differences, differentials, and other calculated inputs directly from the raw data, the training time for the network can be reduced considerably if these values are provided as inputs. The predicted variable was the density of the ester. The neural network model was trained for discrete numbers of methylene groups over the entire temperature range of 300-500 K. The... 

Sion to our assumptions about the initial purchase price and the cost of gasoline. Figure 1 shows the LCC of the hybrid and the conventional car over the ten-year period as a function of the cost of gasoline. When gas prices are approximately 3 per gallon, the two cars cost about the same. This value is referred to as the break-even point. If gas prices reach 3.75 per gallon, the approximate cost in Japan, the hybrid car is more economical. Sensitivity analysis can also be conducted for other input variables, such as initial purchase price, miles driven per year and actual fuel economy. 

The input variables state now no longer jumps abruptly from one state to the next, but loses value in one membership function while gaining value in the next. At any one time, the truth value of the indoor or outdoor temperature will almost always be m some degree part of two membership functions ... 

A general method has been developed for the estimation of model parameters from experimental observations when the model relating the parameters and input variables to the output responses is a Monte Carlo simulation. The method provides point estimates as well as joint probability regions of the parameters. In comparison to methods based on analytical models, this approach can prove to be more flexible and gives the investigator a more quantitative insight into the effects of parameter values on the model. The parameter estimation technique has been applied to three examples in polymer science, all of which concern sequence distributions in polymer chains. The first is the estimation of binary reactivity ratios for the terminal or Mayo-Lewis copolymerization model from both composition and sequence distribution data. Next a procedure for discriminating between the penultimate and the terminal copolymerization models on the basis of sequence distribution data is described. Finally, the estimation of a parameter required to model the epimerization of isotactic polystyrene is discussed. 

Special considerations are required in estimating paraimeters from experimental measurements when the relationship between output responses, input variables and paraimeters is given by a Monte Carlo simulation. These considerations, discussed in our first paper 1), relate to the stochastic nature of the solution and to the fact that the Monte Carlo solution is numerical rather than functional. The motivation for using Monte Carlo methods to model polymer systems stems from the fact that often the solution... 

Iman RL, Helton JC, Campbell JE. An approach to sensitivity analysis of computer models Part II—Ranking of input variables, response surface validation, distribution effect and technique synopsis. / Quality Technol 1981 13 232-40. 

C. Pattern Recognition with Single Input Variable. 

D. Pattern Recognition with Multiple Input Variables. . . . ... 

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