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Essentially, fuzzy theory has been explored from a wide variety of perspectives and applied successfully within many disparate research areas [ 11, 12, 15 ]. Additionally, there has been research into scientific and technological aspects of fuzzy theory by L. Zhang [ 14 ] and C. Chen [ 3 ].

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Fuzzy theory is applied to evaluation, though it lacks sufficient flexibility for evaluation of the main subject based on targets, stages or levels of evaluation, to fill which void, the approach of combined comprehensive evaluation model and the fuzzy control is applied for flexible and comprehensive green industry evaluation. This paper addresses several issues as follows. The green industry evaluation index set is presented in Section 3. The establishment of evaluation model is designed in Section 4.

The application of green industry evaluation model is conducted in Section 5. And Section 6 presents the conclusion. The key to green industry assessment is choice of evaluation indices system, selected based on the characteristics and evaluation of the main objective facts. The indices chosen comprehensively integrate those of foreign authorities [ 5, 7, 16—18 ] with those of domestic authority, according to the characteristics of the domestic green industry [ 1, 13 ].

There are many methods to determine the environmental weight of the green industry, which are best selected according to specific characteristics and particulars of the situation. We applied the analytic hierarchy process to determine green industry impact, due to a hierarchy inherent in the factors shown above. The elements of each layer with respect to weighted layer criteria were deduced according to the maximum Eigen values and their Eigenvectors, applicable to the above matrix. Let Z denote the reviews ratings, i.

Let p denote the number of the evaluation objects. If the index is in the middle of these evaluation levels, [ 2, 4, 6, 8, 10 ] can also be used.

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The fuzzy evaluation matrix was established to build an evaluation index, where the degree, r ij , of B ij belonging to comment, t , was obtained by expert evaluation method or the actual survey methods. The determined membership should then be divided into qualitative and quantitative indicators. Quantitative indicators may be divided into three types to determine their value: partial small, partial large and intermediate. To determine the appropriate membership functions, specific characteristics of the object may be compared with the above three class-types. Membership functions may then be determined by fuzzy statistical method or fuzzy distribution.

Following are trapezoidal distribution formulas. The total of the fuzzy comprehensive evaluation model is given as Equation 4. The fuzzy control rule, which is actually stated set of multiple conditions, is an important part of the fuzzy control [ 4 ]. The data for this evaluation came from Dalian municipality, spanning —, selecting of expert evaluation method to evaluate the data and indicators.

Due to space limitations, only the data from is given here to detail the evaluation process. In order to reduce the deviation of evaluated results, before evaluating green industry development, we applied the robust PLS approach [ 20 ], for prediction and diagnosis against outliers and missing KPI-related data. During the evaluation process, qualitative variables take five evaluation ratings.

Index systems were rated using the Delphi method to represent expert opinion. The level proportion method was used to grade indexes. Statistical results are shown as follows.

### Industrial Applications of Fuzzy Control

Based on the data above, the final results can be calculated as follows. In order to ensure the accuracy and reliability of the evaluation results, this paper applied FDI method [ 21 ] to process the entire data inspection. Since this data contains qualitative variables, many of which are factors beyond measure, this paper references data-driven design of robust fault detection system [ 19 ] to test the entire inspection process. Results from the preceding showed no data-processing error, which indicates credible evaluation results.

Evaluation results can be obtained by fuzzy control rules. We believe this is to be caused by the rapid pace of industrial development causing increased waste emissions, while not being effectively controlled, thus reducing the level of evaluation. Additionally, the ratings for and were similar, due either to the number of factories reaching a stable level or effectively controlling waste emissions with an increase in number of factories. Nevertheless, the level of the green industry was in decline, and environmental pollution also may be intensified.

As it can be seen from the above examples, the evaluated level of green industry in Dalian was low and showed a downward trend, suggesting that the local government should intensify supervision and management, and should encourage green business innovation and improvement, making for harmonious economic and environmental development.

Environmental protection and economic development are equally important for the survival of mankind. We cannot simply focus on the development of our basic needs while ignoring the quality of living space. Notice how each rule provides a result as a truth value of a particular membership function for the output variable. In centroid defuzzification the values are OR'd, that is, the maximum value is used and values are not added, and the results are then combined using a centroid calculation.

Fuzzy control system design is based on empirical methods, basically a methodical approach to trial-and-error. The general process is as follows:. As a general example, consider the design of a fuzzy controller for a steam turbine. The block diagram of this control system appears as follows:. In practice, the controller accepts the inputs and maps them into their membership functions and truth values. These mappings are then fed into the rules. If the rule specifies an AND relationship between the mappings of the two input variables, as the examples above do, the minimum of the two is used as the combined truth value; if an OR is specified, the maximum is used.

The appropriate output state is selected and assigned a membership value at the truth level of the premise. The truth values are then defuzzified.

For an example, assume the temperature is in the "cool" state, and the pressure is in the "low" and "ok" states. The pressure values ensure that only rules 2 and 3 fire:. The output value will adjust the throttle and then the control cycle will begin again to generate the next value. A fuzzy set is defined for the input error variable "e", and the derived change in error, "delta", as well as the "output", as follows:.

These rules are typical for control applications in that the antecedents consist of the logical combination of the error and error-delta signals, while the consequent is a control command output. The rule outputs can be defuzzified using a discrete centroid computation:.

If you have problems figuring out the centroid equation, remember that a centroid is defined by summing all the moments location times mass around the center of gravity and equating the sum to zero. If they are not the same, i. This system can be implemented on a standard microprocessor, but dedicated fuzzy chips are now available.

A block diagram of the chip is shown below:. As a first example, consider an anti-lock braking system , directed by a microcontroller chip. The microcontroller has to make decisions based on brake temperature , speed , and other variables in the system. The variable "temperature" in this system can be subdivided into a range of "states": "cold", "cool", "moderate", "warm", "hot", "very hot".

## Fuzzy Logic Foundations and Industrial Applications : Da Ruan :

The transition from one state to the next is hard to define. An arbitrary static threshold might be set to divide "warm" from "hot". For example, at exactly 90 degrees, warm ends and hot begins. But this would result in a discontinuous change when the input value passed over that threshold. The transition wouldn't be smooth, as would be required in braking situations.

The way around this is to make the states fuzzy. That is, allow them to change gradually from one state to the next. In order to do this there must be a dynamic relationship established between different factors. With this scheme, the input variable's state no longer jumps abruptly from one state to the next. Instead, as the temperature changes, it loses value in one membership function while gaining value in the next. In other words, its ranking in the category of cold decreases as it becomes more highly ranked in the warmer category.

At any sampled timeframe, the "truth value" of the brake temperature will almost always be in some degree part of two membership functions: i. The above example demonstrates a simple application, using the abstraction of values from multiple values. This only represents one kind of data, however, in this case, temperature.

Adding additional sophistication to this braking system, could be done by additional factors such as traction , speed, inertia , set up in dynamic functions, according to the designed fuzzy system. In spite of the appearance there are several difficulties to give a rigorous logical interpretation of the IF-THEN rules.

Then we can translate this system into a fuzzy program P containing a series of rules whose head is "Good x,y ". The interpretation of this predicate in the least fuzzy Herbrand model of P coincides with f.

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This gives further useful tools to fuzzy control. From Wikipedia, the free encyclopedia. For the rock band, see Fuzzy Control band. This article includes a list of references , but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. May Learn how and when to remove this template message. This article reads like a textbook and may require cleanup. Please help to improve this article to make it neutral in tone and meet Wikipedia's quality standards.

February See also: fuzzy set. Fuzzy control and fuzzy systems 2 ed.