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Get Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA PDF

By Pedro Ponce-Cruz

ISBN-10: 3319266551

ISBN-13: 9783319266558

ISBN-10: 331926656X

ISBN-13: 9783319266565

This publication is a entire creation to LabVIEW FPGA™, a package deal permitting the programming of clever electronic controllers in box programmable gate arrays (FPGAs) utilizing graphical code. It indicates how either power problems with realizing and programming in VHDL and the resultant hassle and slowness of implementation may be sidestepped.

The textual content features a transparent theoretical clarification of fuzzy common sense (type 1 and kind 2) with case stories that enforce the speculation and systematically display the implementation method. It is going directly to describe easy and complex degrees of programming LabVIEW FPGA and express how implementation of fuzzy-logic keep watch over in FPGAs improves process responses.

A whole toolkit for enforcing fuzzy controllers in LabVIEW FPGA has been built with the publication in order that readers can generate new fuzzy controllers and install them instantly. difficulties and their strategies let readers to perform the ideas and to take in the theoretical rules as they arise.

Fuzzy common sense sort 1 and kind 2 in line with LabVIEW FPGA™, is helping scholars learning embedded keep an eye on structures to layout and application these controllers extra successfully and to appreciate some great benefits of utilizing fuzzy common sense in doing so. Researchers operating with FPGAs locate the textual content helpful as an creation to LabVIEW and as a device assisting them layout embedded systems.

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Extra info for Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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0 þ 10 þ 20Þ Ã 0:1 þ ð30 þ 40 þ 50 þ 60Þ Ã 0:2 þ ð70 þ 80 þ 90 þ 100Þ Ã 0 0:1 þ 0:1 þ 0:1 þ 0:2 þ 0:2 þ 0:2 þ 0:2 þ 0 þ 0 þ 0 þ 0 ¼ 34:45 y¼ Fig. 20 Speed variable Fig. 10 Basic Numerical Example (TSK) 35 Basic Numerical Example (TSK) Consider that the previous controller uses a TSK inference model for simplifying the computation of the output. The same input shapes are used and the rules are the same. The next figure shows the output points selected: The following rules are activated and the results are: IF TEMPERATURE IS COLD AND RELATIVE HUMIDITY IS MEDIUM THEN SPEED IS LOW lspeed ðlowÞ ¼ min½lðtemperature ¼ coldÞ; lðr humidity ¼ mediumފ ¼ min½0:1; 0:58Š ¼ 0:1 IF TEMPERATURE IS MEDIUM AND RELATIVE HUMIDITY IS MEDIUM THEN THE SPEED IS MEDIUM lspeed ðmediumÞ ¼ min½lðtemperature ¼ mediumÞ; lðr humidity ¼ mediumފ ¼ min½0:2; 0:58Š ¼ 0:2 IF TEMPERATURE IS HIGH AND RELATIVE HUMIDITY IS HIGH THEN THE SPEED IS HIGH À Á lspeed ðhighÞ ¼ minð½lðtemperature ¼ highÞ; l rhumidity ¼ high Š ¼ min½0; 0:5Š ¼ 0 The outputs are then cut in the membership value (dashed lines) in Fig.

As the original algorithm, the left and right parts yl and yr needs to be calculated and then the average of both is the defuzzified crisp value. The optimal initial switch point for the first iteration can be expressed as: hw þ w i i i x i i¼1 2 yl ¼ P hw þ w i PN N i¼1 i ð1:53Þ i 2 where wi the match is weight of the respective xi (the upper membership function value of the FOU) and wi is the match weigh of the lower membership function according to Fig. 25. 48 1 Literature Review for Digital Implementations of Fuzzy Logic … Fig.

14. , min-max). Step 3: Aggregation of the rules outputs anddefuzzification. The next step consists of the evaluation of the output (see Fig. 15). If Z1 = 1, Z2 = 2, and Z3 = 3. Fig. 8 Takagi-Sugeno-Kang 31 Fig. 14 Rule evaluation step Fig. 15 Defuzzification step for TSK inference model The output is calculated by Output ¼ ð0:2 Ã 1Þ þ ð0:1 Ã 2Þ þ ð0:5 Ã 3Þ ð0:5 þ 0:2 þ 0:1Þ An example about fuzzy logic control is presented below. IF TEMPERATUREIS HIGH ANDPRESSURE IS LOWTHEN SET THEVALVE TO K1 The heuristic rule for temperature and pressure control is derived from people experience.

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Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA by Pedro Ponce-Cruz


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