An explanatory variable is the "input" variable and the response variable is the "output" variable.
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How you segment data or choose an output variable, for example, may affect predictive fairness across different sub-populations.
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A variable in the VFSM environment may be activated by actions - in such a case it is an output variable. For instance, a digital output has two actions: True and False. A numerical (analog) output variable has an action: Set. A timer which is both: an input and output variable can be triggered by actions like: Start, Stop or Reset.
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In algebraic models, the output variable is computed by solving algebraic equations.
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The controller then communicates to the regulator what action is needed to ensure that the output variable value is matching the desired value. Therefore, there is a high degree of assurance that the output variable can be maintained at the desired level. The closed- loop control system can be a feedback or a feed forward system: A feedback closed-loop system has a feed-back mechanism that directly relates the input and output signals. The feed-back mechanism monitors the output variable and determines if additional correction is required.
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In transcendental models, the output variable is computed by solving transcendental equations, namely equations involving trigonometric, inverse trigonometric, exponential, logarithmic, and/or hyperbolic functions.
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It frequently is ambiguous just what type of feedback is involved in an amplifier, and the asymptotic gain approach has the advantage/disadvantage that it works whether or not you understand the circuit. Figure 6 indicates the output node, but does not indicate the choice of output variable. In what follows, the output variable is selected as the short-circuit current of the amplifier, that is, the collector current of the output transistor. Other choices for output are discussed later.
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A final clause is appended with a single literal: the final gate's output variable. If this literal is complemented, then the satisfaction of this clause enforces the output expression's to false; otherwise the expression is forced true.
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In this example, the two input variables are "brake temperature" and "speed" that have values defined as fuzzy sets. The output variable, "brake pressure" is also defined by a fuzzy set that can have values like "static" or "slightly increased" or "slightly decreased" etc.
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The variable-frequency drive controls the speed of compressor motor. The compressor is specifically designed to run at different motor speeds to modulate cooling output. Variable speed operation requires an appropriate compressor for full speed operation and a special compressor lubrication system. Proper oil management is a critical requirement to ensure compressor lifetime.
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The output variable value that is fed backward is used to initiate that corrective action on a regulator. Most control loops in the industry are of the feedback type. In a feed-forward closed loop system, the measured process variable is an input variable. The measured signal is then used in the same fashion as in a feedback system.
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The purpose of a PL/SQL function is generally used to compute and return a single value. This returned value may be a single scalar value (such as a number, date or character string) or a single collection (such as a nested table or array). User-defined functions supplement the built-in functions provided by Oracle Corporation. The PL/SQL function has the form: CREATE OR REPLACE FUNCTION [(input/output variable declarations)] RETURN return_type [AUTHID ] \-- heading part amount number; -- declaration block BEGIN -- executable part RETURN ; [Exception none] RETURN ; END; Pipe-lined table functions return collections and take the form: CREATE OR REPLACE FUNCTION [(input/output variable declarations)] RETURN return_type [AUTHID ] [] [declaration block] BEGIN PIPE ROW ; RETURN; [Exception exception block] PIPE ROW ; RETURN; END; A function should only use the default IN type of parameter.
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When c goes from 1 to 0 it "traps" d = q's value and this continues to appear at q no matter what d does (as long as c remains 0). Without delay, inconsistencies must be eliminated from a truth table analysis. With the notion of "delay", this condition presents itself as a momentary inconsistency between the fed-back output variable q and p = qdelayed.
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A cycloconverter constructs an output, variable-frequency, approximately sinusoid waveform by switching segments of the input waveform to the output; there is no intermediate DC link. With switching elements such as SCRs, the output frequency must be lower than the input. Very large cycloconverters (on the order of 10 MW) are manufactured for compressor and wind-tunnel drives, or for variable-speed applications such as cement kilns.
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Evolutionary programming is often paired with other algorithms e.g. ANN to improve the robustness, reliability, and adaptability. Evolutionary models reduce error rates by allowing the numerical values to change within the fixed structure of the program. Designers provide their algorithms the variables, they then provide training data to help the program generate rules defined in the input space that make a prediction in the output variable space.
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Control methods employ sensors to measure the output variable of the device and provide feedback to the controller so that it can make corrections toward desired performance. Automatic control manages a device without the need of human inputs for correction, such as cruise control for regulating a car's speed. Control systems engineering activities are multi-disciplinary in nature. They focus on the implementation of control systems, mainly derived by mathematical modeling.
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A transmitter is a device that produces an output signal, often in the form of a 4–20 mA electrical current signal, although many other options using voltage, frequency, pressure, or ethernet are possible. The transistor was commercialized by the mid-1950s. Instruments attached to a control system provided signals used to operate solenoids, valves, regulators, circuit breakers, relays and other devices. Such devices could control a desired output variable, and provide either remote or automated control capabilities.
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A simple but useful tool is to plot scatter plots of the output variable against individual input variables, after (randomly) sampling the model over its input distributions. The advantage of this approach is that it can also deal with "given data", i.e., a set of arbitrarily-placed data points, and gives a direct visual indication of sensitivity. Quantitative measures can also be drawn, for example by measuring the correlation between Y and Xi, or even by estimating variance-based measures by nonlinear regression.
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A shift invariant system is the discrete equivalent of a time-invariant system, defined such that if y(n) is the response of the system to x(n), then y(n-k) is the response of the system to x(n-k).Oppenheim, Schafer, 12 That is, in a shift-invariant system the contemporaneous response of the output variable to a given value of the input variable does not depend on when the input occurs; time shifts are irrelevant in this regard.
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From the Economics community, the independent variables are also called exogenous. Depending on the context, a dependent variable is sometimes called a "response variable", "regressand", "criterion", "predicted variable", "measured variable", "explained variable", "experimental variable", "responding variable", "outcome variable", "output variable", "target" or "label".. In economics endogenous variables are usually referencing the target. "Explanatory variable" is preferred by some authors over "independent variable" when the quantities treated as independent variables may not be statistically independent or independently manipulable by the researcher.Everitt, B.S. (2002) Cambridge Dictionary of Statistics, CUP.
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It is clear that variation of the groundwater levels have significant power at the ocean tidal frequencies. To estimate the extent at which the groundwater levels are influenced by the ocean surface levels, we compute the coherence between them. Let us assume that there is a linear relationship between the ocean surface height and the groundwater levels. We further assume that the ocean surface height controls the groundwater levels so that we take the ocean surface height as the input variable, and the groundwater well height as the output variable.
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IF-THEN rules map input or computed truth values to desired output truth values. Example: IF temperature IS very cold THEN fan_speed is stopped IF temperature IS cold THEN fan_speed is slow IF temperature IS warm THEN fan_speed is moderate IF temperature IS hot THEN fan_speed is high Given a certain temperature, the fuzzy variable hot has a certain truth value, which is copied to the high variable. Should an output variable occur in several THEN parts, then the values from the respective IF parts are combined using the OR operator.
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The regulator is able to alter the input variable in response to the signal from the controller. An open-loop system has no feedback or feed forward mechanism, so the input and output signals are not directly related and there is increased traffic variability. There is also a lower arrival rate in such system and a higher loss rate. In an open control system, the controllers can operate the regulators at regular intervals, but there is no assurance that the output variable can be maintained at the desired level.
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Skirbekk has focused on studying health, productivity, and associated determinants from a multidisciplinary perspective with an emphasis on the role of changing labor market demands, technological and cultural changes as well as variation in the attitudes, beliefs, and competences of new cohorts. From considering productivity as an output variable (e.g., measured as value-added, salary levels), a key contribution of his research has been to highlight the integral role of productivity determinants (such as skills, health, and abilities). This research has helped change the focus of age-variation in productivity from something fixed to an entity that is to a greater extent modifiable.
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Thus, it is a generalization of both interval analysis and probability theory. The diverse methods comprising probability bounds analysis provide algorithms to evaluate mathematical expressions when there is uncertainty about the input values, their dependencies, or even the form of mathematical expression itself. The calculations yield results that are guaranteed to enclose all possible distributions of the output variable if the input p-boxes were also sure to enclose their respective distributions. In some cases, a calculated p-box will also be best-possible in the sense that the bounds could be no tighter without excluding some of the possible distributions.
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In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term. Together with the autoregressive (AR) model, the moving- average model is a special case and key component of the more general ARMA and ARIMA models of time series, which have a more complicated stochastic structure. The moving-average model should not be confused with the moving average, a distinct concept despite some similarities.
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Ferson, S., R. Nelsen, J. Hajagos, D. Berleant, J. Zhang, W.T. Tucker, L. Ginzburg and W.L. Oberkampf (2004). Dependence in Probabilistic Modeling, Dempster–Shafer Theory, and Probability Bounds Analysis. Sandia National Laboratories, SAND2004-3072, Albuquerque, NM. These methods, collectively called probability bounds analysis, provide algorithms to evaluate mathematical expressions when there is uncertainty about the input values, their dependencies, or even the form of mathematical expression itself. The calculations yield results that are guaranteed to enclose all possible distributions of the output variable if the input p-boxes were also sure to enclose their respective distributions.
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In control engineering and control theory the transfer function is derived using the Laplace transform. The transfer function was the primary tool used in classical control engineering. However, it has proven to be unwieldy for the analysis of multiple-input multiple-output (MIMO) systems, and has been largely supplanted by state space representations for such systems. In spite of this, a transfer matrix can always be obtained for any linear system, in order to analyze its dynamics and other properties: each element of a transfer matrix is a transfer function relating a particular input variable to an output variable.
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In electronics and electrical engineering, a ramp generator is a circuit that creates a linear rising or falling output with respect to time. The output variable is usually voltage, although current ramps can be created. Linear ramp generators are also known as sweep generators Ramp generators produces a sawtooth wave form, Suppose a 3V is applied to input of a comparator of X terminal and ramp generator at Y terminal. the ramp generator starts increasing its voltage but, still lower than input X terminal of the comparator the output shall be 1, As soon as the ramp voltage is equal to or more than X, comparator output goes low.
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Transferring the knowledge from a large to a small model needs to somehow teach to the latter without loss of validity. If both models are trained on the same data, the small model may have insufficient capacity to learn a concise knowledge representation given the same computational resources and same data as the large model. However, some information about a concise knowledge representation is encoded in the pseudolikelihoods assigned to its output: when a model correctly predicts a class, it assigns a large value to the output variable corresponding to such class, and smaller values to the other output variables. The distribution of values among the outputs for a record provides information on how the large model represents knowledge.
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If the input function is in closed-form and the desired output function is a series of ordered pairs (for example a table of values from which a graph can be generated) over a specified domain, then the Fourier transform can be generated by numerical integration at each value of the Fourier conjugate variable (frequency, for example) for which a value of the output variable is desired.. Note that this method requires computing a separate numerical integration for each value of frequency for which a value of the Fourier transform is desired... The numerical integration approach works on a much broader class of functions than the analytic approach, because it yields results for functions that do not have closed form Fourier transform integrals.
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The primary use of output parameters is to return multiple values from a function, while the use of input/output parameters is to modify state using parameter passing (rather than by shared environment, as in global variables). An important use of returning multiple values is to solve the semipredicate problem of returning both a value and an error status – see Semipredicate problem: Multivalued return. For example, to return two variables from a function in C, one may write: int width int height; F(x, &width;, &height;); where `x` is an input parameter and `width` and `height` are output parameters. A common use case in C and related languages is for exception handling, where a function places the return value in an output variable, and returns a boolean corresponding to whether the function succeeded or not.
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The control rules for estimating module values are based on logic relationships between inputs and outputs, expressed in linguistic terms by 'if-then' statements. For example, when two input variables (validation metrics) are aggregated four rules are required, formalized as: ____PREMISE____CONCLUSION if x1 is F and x2 is F then yi is B1 if x1 is F and x2 is U then y2 is B2 if x1 is U and x2 is F then y3 is B3 if x1 is U and x2 is U then y4 is B4 where xi is an input variable, yi is an output variable and Bi is a conclusion (or expert weight). The value of each conjunction (… and …) is the minimum of the quantified fuzzy groups, which are obtained from complementary S-shaped distribution curves.
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The concept of the index of a DVI is important and determines many questions of existence and uniqueness of solutions to a DVI. This concept is closely related to the concept of index for differential algebraic equations (DAE's), which is the number of times the algebraic equations of a DAE must be differentiated in order to obtain a complete system of differential equations for all variables. It is also a notion close to the relative degree of Control Theory, which is, roughly speaking, the number of times an "output" variable has to be differentiated so that an "input" variable appears explicitly in Control Theory this is used to derive a canonical state space form which involves the so-called "zero-dynamics", a fundamental concept for control). For a DVI, the index is the number of differentiations of F(t, x, u) = 0 needed in order to locally uniquely identify u as a function of t and x.
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In statistics, econometrics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence relation which should not be confused with differential equation). Together with the moving-average (MA) model, it is a special case and key component of the more general autoregressive–moving-average (ARMA) and autoregressive integrated moving average (ARIMA) models of time series, which have a more complicated stochastic structure; it is also a special case of the vector autoregressive model (VAR), which consists of a system of more than one interlocking stochastic difference equation in more than one evolving random variable. Contrary to the moving-average (MA) model, the autoregressive model is not always stationary as it may contain a unit root.
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If the input function is a series of ordered pairs (for example, a time series from measuring an output variable repeatedly over a time interval) then the output function must also be a series of ordered pairs (for example, a complex number vs. frequency over a specified domain of frequencies), unless certain assumptions and approximations are made allowing the output function to be approximated by a closed-form expression. In the general case where the available input series of ordered pairs are assumed be samples representing a continuous function over an interval (amplitude vs. time, for example), the series of ordered pairs representing the desired output function can be obtained by numerical integration of the input data over the available interval at each value of the Fourier conjugate variable (frequency, for example) for which the value of the Fourier transform is desired.. Explicit numerical integration over the ordered pairs can yield the Fourier transform output value for any desired value of the conjugate Fourier transform variable (frequency, for example), so that a spectrum can be produced at any desired step size and over any desired variable range for accurate determination of amplitudes, frequencies, and phases corresponding to isolated peaks.
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