However, while numerous applications of intelligent control IC have been described in the literature, few advance past the simulation stage to become laboratory prototypes, and only a handful make their way into products.

Thus, IC is designed to seek control methods that provide a level of intelligence and autonomy in the control decision that allows for improving the system performance. Even though IC is a relatively new technique, a huge number of industrial applications have been developed. IC has different tools for emulating the biological behavior that could solve problems as human beings do. The main tools for IC are presented below:. These movements represent what is left over in a time series after the other components have been accounted for.

These components do not always occur alone, they can occur in any combination or all together, for this reason no single best forecasting model exists. Thus, one of the most important problems to be solved in forecasting is that of trying to match the appropriate model to the model of the available time series data. Some applications related to this topic are summarized in the following: Stock index prediction. Companies or governments need to know about their re- sources in stock.

## Artificial Intelligent Fuzzy Logic Controller Applied on 6DOF Robot Arm Using LabVIEW and FPGA

This is why predictors are constantly used in those places. In gen- eral, they are looking for some patterns about the potential market and then they have to offer their products. In these terms, they want to know how many products could be offered in the next few months.

Statistically, this is possible with predictors or forecasters knowing the behavior of past periods. For example, Shen [1] reports a novel predictor based on g ray models using some n eural networks. Actually, this model was used to predict the monetary changes in Shanghai in the years and Other applications in stock index forecasting are reported in [1]. Box—Jenkins forecasting in Singapore. Dealing with construction industry de- mand, Singapore needed to evaluate the productivity of this industry, its construc- tion demand, and tend prices in the year This forecasting was applied with a Box—Jenkins model.

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Goa and H. Teo [2]. Pole assignment controller for practical applications. In the industry, controllers are useful in automated systems, industry production, robotics, and so on. In these terms, a typical method known as generalized minimum variance control GMVC is used that aims to self-tune its parameters depending on the application. However, this method is not implemen ted easily. In Mexico, researchers designed a practical 7. They used the minimum variance control technique to achieve this. Inventory control. In the case of inventory control, exponential smoothing fore- casters are commonly used.

As an example of this approach, Snyder et al. Dry kiln transfer function. Practical applications have non- linear relations between their input and output variables. However, transfer functions cannot be applied in that case because it has an inherent linear property. Forecast- ing is then used to set a function of linear combinations in statistical parameters. Blankenhorn et al.

Then, classical control techniques could be applied. These methods are usually necessary when historical data is not available or is scarce. They are also used to predict changes in historical data patterns. Since the use of historical data to predict future events is based on the assumption that the pattern of the historical data will persist, changes in the data pattern cannot be predicted on the basis of historical data. Thus, qualitative methods are used to predict such changes.

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Some of these techniques are: 1. Depending on the knowledge of an expert a curve is built to forecast the response of a variable, thus this expert must have a great deal of expertise and judgment.

follow site Delphi method. The members are physically separated, they have to respond to a series of questionnaires, and then subsequent questionnaires are accompa- nied by information concerning the opinions of the group. They can be grouped into two kinds: univariate and causal models. The univariate model predicts future values of a time series by only taking into account the past values of the time series.

Historical data is analyzed attempting to identify a data pattern, and then it is assumed that the data will continue in the future and this pattern is extrapolated in order to produce forecasts. Therefore they are used when conditions are expected to remain the same. The statistical model is used to forecast the desired variable. The variable of interest or dependent variable.

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The objective then is to use a regression model and use it to describe, predict or control the dependent variables on the basis of the independent variables. Regression models can employ quantitative or qualitative independent vari- ables. Quantitative independent variables assume numerical values corresponding to points on the real line. Qualitative independent variables are non-numerical. The models are then developed using observed models of the dependent and independent variables.

If these values are observed over time, the data is called a time series.

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If the values are observed at one point in time, the data are called cross-sectional data. This unequal weighting is accomplished by one or more smoothing constants, which determine how much weight is given to each ob- servation. It has been found to be most effective when the parameters describing the time series may be changing slowly over time. Exponential smoothing methods are not based on any formal model or theory; they are techniques that produce adequate forecasts in some applications. Since these techniques have been developed without a theoretical background some prac- titioners strongly object to the term model in the context of exponential smoothing.

This method assumes that the time series has no trend while the level of the time series may change slowly over time.

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Instead, it may be desir- able to weight recent observations more heavily than remote observations. Simple- exponential smoothing is a forecasting method that applies unequal weights to the time series observations. This is accomplished by using a smoothing constant that determines how much weight is given to the observation.

Usually the most recent is given the most weight, and older observations are given successively smaller weights. Then the estimate a 0. A If we observe y T C1 in the time period T C 1, we can update a 0. The decision to change smoothing constants can be made by employing adaptive control procedures.

By using a tracking signal we will have better results in the forecasting, by realizing that the forecast error is larger than an accurate forecasting system might reasonably produce. We will suppose that we have accumulated the T single-period-ahead forecast errors e 1. With that we will have Y. By that we understand that the forecasting system is producing errors that are either consistently positive or negative.