Auto engine Honda, Nissan Use to select geat based on engine load, driving style, and road conditions.
Copy machine Canon Using for adjusting drum voltage based on picture density, humidity, and temperature. Cruise control Nissan, Isuzu, Mitsubishi Use it to adjusts throttle setting to set car speed and acceleration Dishwasher Matsushita Use for adjusting the cleaning cycle, rinse and wash strategies based depend upon the number of dishes and the amount of food served on the dishes.
Elevator control Fujitec, Mitsubishi Electric, Toshiba Use it to reduce waiting for time-based on passenger traffic Golf diagnostic system Maruman Golf Selects golf club based on golfer's swing and physique. Fitness management Omron Fuzzy rules implied by them to check the fitness of their employees. What is Online Analytical Processing?
OLAP is a category of software that allows users to analyze Following are frequently asked questions in interviews for freshers as well experienced ETL tester and What is Teradata? Teradata is massively parallel open processing system for developing large-scale data What is Data Modelling?
Data modeling is the process of creating a data model for the data to be Home Testing. Must Learn! Big Data. Live Projects. Tableau can create interactive visualizations customized for the target audience. In this What is Data Mart?
Fuzzy Systems - A Tutorial
A data mart is focused on a single functional area of an organization and Use fuzzy logic to controls brakes in hazardous cases depend on car speed, acceleration, wheel speed, and acceleration. When should you not use fuzzy logic? The safest statement is the first one made in this introduction: fuzzy logic is a convenient way to map an input space to an output space. If you find it's not convenient, try something else. If a simpler solution already exists, use it. Fuzzy logic is the codification of common sense — use common sense when you implement it and you will probably make the right decision.
Many controllers, for example, do a fine job without using fuzzy logic.
However, if you take the time to become familiar with fuzzy logic, you'll see it can be a very powerful tool for dealing quickly and efficiently with imprecision and nonlinearity. You can create and edit fuzzy inference systems with Fuzzy Logic Toolbox software. You can create these systems using graphical tools or command-line functions, or you can generate them automatically using either clustering or adaptive neuro-fuzzy techniques. The toolbox also lets you run your own stand-alone C programs directly. You can customize the stand-alone engine to build fuzzy inference into your own code.
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Why Fuzzy Logic?
Related Topics. What Is Fuzzy Logic? Description of Fuzzy Logic In recent years, the number and variety of applications of fuzzy logic have increased significantly.
Fuzzy logic is conceptually easy to understand. Fuzzy logic is flexible. Fuzzy logic is tolerant of imprecise data. Fuzzy logic can model nonlinear functions of arbitrary complexity. Fuzzy logic can be built on top of the experience of experts.
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Fuzzy logic can be blended with conventional control techniques. Mathematically, fitting a curve of lower order would produce a fairly inaccurate representation. Therefore, a higher order curve fit would be appropriate to accommodate the noisy signal. Fuzzy Logic attempts to emulate what the human response would be and apply the most intelligent fit to the data.
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Currently there are many applications of Fuzzy Logic utilized by common household devices, products which most people are familiar with. The benefit of Fuzzy Logic becomes transparent to the user of consumer devices since the Fuzzy Module or function is embedded within the product. The advantage of this approach takes the need for the operator to understand the theory of fuzzy operation away. Operation only requires the application of common knowledge to the standard parameters. A few products which have benefited from the implementation of Fuzzy Logic are: camcorders with automatic compensation for operator injected noise such as shaking and moving; elevators with decreased wait time, making intelligent floor decisions and minimizing travel and power consumption; anti lock braking systems with quick reacting independent wheel decisions based on current and acquired knowledge; television with automatic color, brightness, and acoustic control based on signal and environmental conditions; and finally, most importantly to this article, single loop temperature and process controls.
Ziegler-Nichols control theory provides for PID proportional, integral, and derivative numbers which aid in the operation of controls.
Most controllers that are microprocessor based have an autotune function which operates a system experiment as shown in figure 2. This experiment helps to determine the thermal characteristics of a particular system. In most cases, the method of autotune is to make a step input into the final control element and monitor the output.
This produces a gain term directly related to proportional band.
A delay time between the application of the step input and an observed response influences the derivative number. The rise time of the response to the step input produces a value to be used in integration. In some systems, the delay time to produce response is much different than the time to give up heat as shown in figure 3. This is common with many extruder applications making a Fuzzy Logic approach quite beneficial.
If the response of the final control element as shown in figure 4 is nonlinear, for whatever reason, a linear response from proportioning action only would result in less than acceptable control. In addition, if the system tends to have changing thermal properties or some thermal irregularities, Fuzzy Logic control should offer a better alternative to the constant adjustment of PID parameters.
Most Fuzzy Logic software begins building its information base during the autotune function. In fact, the majority of the information used in the early stages of system startup come from the autotune solutions. Until the 's, using computationally intensive Fuzzy Logic methods of control was not worth the cost to incorporate. As microprocessors become faster and memory becomes cheaper, the benefit to cost ratio has climbed significantly. Looking at the typical response of a standard PID control strategy, shown in figure 5, the response curve demonstrates a quarter wave decay phenomenon.
This method works adequately in the steady state. However, in real applications the overshoot and undershoot may sometimes be unacceptable. Methods derived prior to Fuzzy Logic involve setpoint adjustment to control this oscillation below a critical level. Ramp to setpoint was introduced into single loop controls to reduce the rate of ascent of the process variable in order to knock down the initial overshoot.
Finally, slight detuning of the steady state PID parameters is commonly done to minimize the destructive oscillatory response. Fuzzy Logic can incorporate an intelligent response to deal with these situations.