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F U Z Z G E N Fuzzy Logic Source Code Generator Evaluation Version 1.0 U S E R G U I D E INTRODUCTION: As programmers, many of us have heard the terms NEURAL NETWORKS and FUZZY LOGIC bandied about over the last couple of years. The odd thing is that very few of us that write code on a daily basis really have a handle on what these things mean. Fuzzy Logic is a decision making method for computers. It has taken many years for the merits of Fuzzy Logic to become clear. One of the reasons is the name: people hear "Fuzzy Logic" and immediately assume that it is imprecise. Actually, it is the other way around -- Fuzzy Logic allows FOR imprecision in the input in making a precise decision. The idea that Fuzzy Logic is suited only to microcontroller applications such as flow controllers and such is also quite misleading. It is suitable for a wide variety of applications. Fuzzy Logic also allows for perspective to change without having to completely recode massive programs. For instance, if you have a program like a battle simulation that takes factors such as the effect of heat into account on the troops, the perspective of HOT changes depending on the origin of the troops. Swedish or Russian troops may see 80 degrees F as hot, whereas troops from countries nearer the equator see 100 degrees as hot and 80 degrees as mild. Being able to make this decision in a "Fuzzy" manner means that you can read in the control variables as needed and still guarantee the integrity of a decision made by the program. FuzzGen is a program intended to introduce you to Fuzzy concepts and allow you to create Fuzzy decision sets for your own programs. It allows you to graphically model the decision process to aid in visualising a decision. It also produces source code in PASCAL, BASIC, or C so that you can incorporate Fuzzy decision making in your programs. It is intended to save time so that you do not need to keep coding IF - THEN - ELSE blocks to make decisions in your programs. Essentially, FuzzGen is a CASE tool that is additionally educational in that it will introduce many of you to new concepts. BASIC CONCEPTS: Fuzzy Logic is a little like the "new math" some of us were taught (starting in the late 60's and early 70's in most districts) in school. Fuzzy Logic uses the concept of SETS and membership. What this means is that a data point is seen as either being a set member or not, and more importantly, HOW MUCH of a member. Using triangular and trapezoidal shapes to model data allows you to plot a data point on a slope. Rather than exclusively dealing with YES/NO, you can deal with "65%" or "32%" membership. In essence, you can say that data point X is a Y% member of data set Z. This allows you to have much more control over input data variance, as we can immediately see exactly where input data lies. This allows for a different way to code than what you're used to. (By the way, we'll be using code fragments done in C to illustrate points as we go along.) For instance, here's how you may code for a variable input in standard ways: if(INPUT_DATA <= .1) we_do_this(); else if(INPUT_DATA > .1 && INPUT_DATA <= .2) we_do_that(); else if(INPUT_DATA > .2 && INPUT_DATA <= .3) do_something_else(); // AND SO ON... Fuzzy Logic tends to compartmentalise things. Therefore, instead of a big IF-ELSE construct, you could use the following: int i, SetNumber; double testval, setpctg = 0; for(i = 0; i < NumOfSets; i++){ // loop testval = InSet(i, INPUT_DATA); // is data in the set? if(testval > setpctg){ // if so, setpctg = testval; // reset high pctg marker SetNumber = i; // and record which set. } } do_whatever_we_need_done(SetNumber); What the above code does is record the set with the highest membership percentage so that we have a variable that shows what set the data is in and also what action to take. At first blush, this may not seem any better than the "old" way using the large IF-ELSE construct. However, if you stop and think about it, you have more control with the second method. Sure, we could change the test points to variables in both cases, but the second method does not need to be recompiled if you change your mind about the number of tests to make. This is provided by variable NumOfSets. Now, using the Fuzzy method, you can read your variable list in PLUS you can read in how many tests to take. Sure, you could do this yourself with IF-ELSE constructs, but this is easier. The next step is to make it simpler to model a decision so that it is simple to see what is happening. GRAPHICS AND DECISION VISUALISATION: FuzzGen uses a 2 axis graph to allow you to see data sets and their relationships. The Y axis of the graph is used to show percentage and ranges from 0 - 100. The X axis is used to show the ordinal range of input data values. See figure 1. | 100 | | | <---------------- Percentage | | Ordinal Data | | | | |_0_________________________________________________________ 4 20 -=-=-=-=-=-= 4-20 ma current input =-=-=-=-=- fig 1 Also, to be able to properly plot data values into sets, we use shapes to classify them. Common shapes (supported by FuzzGen) are Triangular, Trapezoidal, and Square (Boolean.) There is infinite variety possible using the Triangular and Trapezoidal shapes. As an example, we'll now graph out a simple Fuzzy Logic demo that shows how to use the graphics and shapes to allow a change of perspective. The subject is temperature as seen by a Polar Bear and as seen by someone's grandmother. ______________________60 82_________ 100 | \ / | | \ / | P | \ / | C | \ / | T | C O L D \ / H O T | G | \ / | | \/ | | /\ | 0 |____________________________/__\_______________| 0 65 70 100 -=-=-=-=TEMPERATURE AS SEEN BY GRANDMA =-=-=-=- fig 2 So, in this scenario, if you wanted to find out what Grandma would think of, say, 62 degrees, you would plot along the X axis to 62 degrees and find out where the decision shape intersected the decision shapes at. In this case here, Grandma would see 62 degrees as 75% COLD and not HOT at all. (It didn't intersect the HOT decision at all.) The COLD decision set uses a Trapezoidal shape: anything from 0 - 60 is seen as 100% COLD, and at 60 degrees it starts tapering down to 70. HOT also uses a trapezoidal shape. _____10 34___________________________ 100 | \ / | | \ / | P | \ / | C | \ / | T | MILD \ / H O T | G | \ / | | \/ | | /\ | 0 |__________/__\_________________________________| 0 20 25 100 -=-=-=-=TEMPERATURE AS SEEN BY A POLAR BEAR =-=-=-=- fig 3 On the other hand, we have in this scenario the EXACT SAME decision shapes used, but now we see the SAME RANGE of the temperature as seen by a Polar Bear. Note that the bear sees anything under 10 degrees as MILD, and anything above this as getting warmer. Downright HOT starts at 34 degrees. In these two cases we see the same data being looked at by use of Fuzzy Logic decisions to base COLD or HOT on the perspective of the viewer. While this is a very simple example, it should serve to explain the basic FuzzGen modeling of Fuzzy Logic. USING FUZZGEN: As the program starts up, it will allow you to either get Help, Open a data file, or start a New file. Once you get a filename started, you are allowed to SETUP the GRAPH. STEP 1: SET UP THE GRAPH BASICS In the graph setup, you may want to put a label on the X axis so that viewing of the graph is a little clearer. You can also optionally put tickmarks and Major Tickmarks along the X axis so that it is easier to see where a decision is intersected. You'll also need to specify the unit range of input data, which is required. For instance, if your input data is in degrees Farenheight, your range may be (like our example) 0 - 100. On the other hand, you may want to create code that reads a hardware current loop device from 4 - 20 milliamps. In other words, the range used is up to you and your application. The last thing to do is tell FuzzGen how many decisions it will need to work with. You may use up to 10 in this evaluation version. All of these settings can be changed later, such as changing your mind about how many decisions to work with and do on. STEP 2: ADD DECISION SHAPES You'll need to access SETUP | DATA SETS. This window allows you to choose a general shape by clicking the associated option button. You can "fine-tune" the triangular and trapezoidal shapes by clicking the picture underneath the option button. Each click will cause the picture to cycle through the possible shapes. As you click the option buttons denoting shape, the data input boxes denoting major hinge points of the shape (i.e. endpoints and apexes) will change to reflect the number of inputs this shape will need: Boolean - 2, Trapezoidal - 4, Triangular - 3. These data input boxes allow for full double-precision data, so you're not limited to integer based decision-making. Once you decide on a basic shape, you can enter the data. FuzzGen may sometimes disallow your input and warn you to that effect. If this happens, look at the basic shape picture to see if it allows you to do what you want. In general, the shapes that do not have a straight side have the ability to place the apex hinge points anywhere you want. Next, you should pick a "Tag colour" by clicking the little box with a colour in it. This allows you to pick a colour so that when the graph is drawn it is easier to associate the particular decision shape with a colour. Once these items are in order, you should click the RECORD button to record the input. You can then select a new decision to enter or edit data for by clicking the LEFT-RIGHT buttons in the lower right. Above the buttons is a label showing you what decision you are working on (numbered) such as "1 of 4." At any time, you can access a SHAPE HELP by clicking the HELP button. The SHAPE HELP window shows you the basics of working with the shapes. As you get more accustomed to working with FuzzGen, you'll rely on this a little less. Once you have essentially defined the decsion shapes, we can then proceed to the next step. STEP 3: MODELING You should now exit from the SETUP DATA SETS window by clicking the CANCEL button. On the main FuzzGen window, click the APPLY button. Your graph and basic data shapes are shown. You may at this time elect to go back to SETUP GRAPH or SETUP DATA SETS to fine tune your model. In this sense, FuzzGen is interactive in that you can make an adjustment and come back to click the APPLY button to see what the change looks like. STEP 4: GENERATE SOURCE CODE The LANGUAGE menu allows you to select a source language to use by clicking the language of choice so that it becomes "checked." If you are satisfied that your application is correctly done (i.e. the graph seems to reflect what you're after) and the language source is correct, go back to the LANGUAGE menu and select the MAKE CODE menu item. This will output a file with the appropriate extension added to the name of your data file. For example, if you are working with filename DUMMY, here's the source files that would be generated depending on your language of choice: C ------- DUMMY.C PASCAL -- DUMMY.PAS BASIC --- DUMMY.BAS Unless you have any second thoughts about the decision modeling, you are pretty much done with FuzzGen at this point. You should SAVE the data file you are working with for later recall. In the FILE menu, select SAVE or SAVE AS. SAVE AS will ask you for a new name to save under (other than the one you used). It will save in the current data file directory. UNDERSTANDING FUZZGEN SOURCE CODE: Essentially, the code example we used earlier was idealised so that you could see how highly optimised Fuzzy code would work. FuzzGen code is slightly different in that we don't know what you'll want to do to optimise it. FuzzGen output source code is fully commented such that you should be able to be able to read it without difficulty. We think that you'll want code you can read cleanly rather than highly optimised code that is more difficult to decipher. As such, here's a short description of what a typical source file would look like: It starts out with a series of constants, which are basically the hinge points of the different decision sets. It uses a coding for the names so that you can tell what decision uses what constants. Here's an example: d1low = decision #1, left (low) endpoint d1hi = decision #1, right (high) endpoint ap1l = APEX #1 LEFT = decision #1 left apex So in a C source output, it would use #defines to set up the initial constants: #define d1low 0 #define d1hi 20 etc... The constant declaration is followed by the source for each of the decisions. They are all named decision-n and you pass the inputs (including the data to test) as part of the parameter list. In C: double Decision1(double TESTDATA, double leftend, double leftapex, double rightapex, double rightend) For immediate implementation, you'd simply feed a decision with the previously defined constants as follows: double percentage; percentage = Decision1( DATA, d1low, ap1l, ap1r, d1hi); This allows you to test each decision independantly. Our recommendation is that once you're satisfied that the routines work as you want, that you combine them into 1 single function as follows: double FuzzDecision( int DecisionNum, double TestData) { switch (DecisionNum){ case 1: BODY OF FUZZGEN DECISION 1 CODE case 2: BODY OF FUZZGEN DECISION 2 CODE .... } This will allow you to make your decision loop much faster as in the example code we had at the beginning of this guide. FUZZGEN DISTRIBUTION: FuzzGen evaluation version 1.0 is a program that will hopefully give you enough of a taste of Fuzzy programming that you'll want to give serious consideration to ordering FuzzGen II, which is the enhanced retail version of this product. FuzzGen II Features: * Printing! You can print graphics copies of your decision logic to aid in documenting programs. * Notes File to attach to the decision logic so that you can document the entire decision process, assumptions, etc. * More decision sets for those ultra-tough projects. * Printed documentation with some more tricks, techniques and hints. FuzzGen is a shareware product, meaning that it has been released in a functional format for BBS and disk vendor distribution. It is as much of an advertising ploy for Alston Software Labs as much as it is useful in and of itself. LEGAL STUFF: Shareware distribution sometimes is misunderstood. For instance, FuzzGen is NOT public domain. It is a copyrighted product wholly owned by Alston Software Labs, and licensees (as in standard retail software) may use FuzzGen to create source code. Although we do not charge any sort of royalty license to use FuzzGen generated source code in your programs, you are strictly forbidden to use Fuzzgen code in your programs if you haven't paid for the product. Sorry, but any money you paid to get a copy of this program from a vendor or other source did not give you any rights to FuzzGen. INDUSTRY STANDARD DISCLAIMER: FuzzGen evaluation version 1.0 is supplied as-is with no warranty whatsoever with regard to merchanability or fitness for purpose. Alston Software Labs assumes no responsibility for any consequenses of use or misuse of this program. HOW TO ORDER: Product purchase comes in 2 forms: You may elect to "register" FuzzGen as-is for $15, at which point we'll send you a fully licensed copy of FuzzGen minus the "Evaluation Copy" tags present in this copy. For $25, you may purchase FuzzGen II, which contains the added features outlined above. Either payment method will license you to be a registered user with rights to use the source code produced by FuzzGen (or FuzzGen II) in your programs without any royalty. Please use WINDOWS NOTEPAD or a similar editor to open ORDER.FRM to be able to print the order form / invoice. You'll need to call or Fax us for Site License information. IN CLOSING: We sincerely hope you'll be able to learn something from FuzzGen, even if you don't order it. We encourage you to pass this product on to associates, employees, and other interested parties. (providing, of course, that you charge no fee for it.) If you have suggestions for improvement of FuzzGen, we'd sure like to hear from you. Our aim is to produce a truly useful Fuzzy Logic generator at the lowest possible end-user cost, and we're always looking for ways to improve our product line. Speaking of product lines, you can see what other ASL products are available by browsing the CATALOG.DOC file using NOTEPAD or a similar tool. Alston Software Labs 1320 Standiford Ave #242 Modesto CA 95350 USA Phone (209) 522 - 8666 FAX (209) 522 - 8666