Products/fuzzyTECH Editions/Editions Overview
created different Editions of fuzzyTECH to
complement the specific needs of your target hardware. This page
provides you with an
overview on the three groups of fuzzyTECH Editions:
Further down on this page you find detailed Technical Specifications for all Editions.
General Purpose fuzzyTECH Edition
also support the special
"plug-and-play" type runtime modules provided for specific process
software such as InTouch™, FactoryLink™,
TheFIX™, Genesis™, and WinCC™.
In addition to the M source code generation provided by every fuzzyTECH
the fuzzyTECH Professional Edition also
contain a MEX runtime module
that provides high-performance computation of fuzzy logic systems in
environment. N.B.: Starting
with release 5.0, the fuzzyTECH Professional
Edition replaces the fuzzyTECH Precompiler Edition.
The following tables provide a summary of the various fuzzyTECH Editions specifications. For a detailed explanation of the terminology used here, download and install the fuzzyTECH Demo from the Download section and use the index function of its Online Help System.
fuzzyTECH is available in different editions to provide the most comprehensive support for your target platform and application area. Due to differences in the capabilities of the supported hardware platforms, technical restrictions apply to the size of the fuzzy systems. The following table shows an overview of the maximum number of interfaces, variables, terms, rule blocks and rules for the different fuzzyTECH Editions. A "-" sign indicates that no practical limit exists. The total number of Variables (Total) represent the number of input, output and intermediate variables of the entire fuzzy logic system. The columns Input and Output show the maximum amount of input and output variables. Terms per Variable relates to the total number of terms for each variable. The column Total Terms shows the maximum number of terms that may occur in one fuzzy logic project. The Rules sections shows the maximum total number of Rule Blocks and Rules that a fuzzy logic project may contain, as well as the maximum total number of input variables (Inputs per RB) and output variables (Outputs per RB) that can be assigned to a rule block.
|Total||Input||Output|| Terms per Variable
|Total Terms|| Rule Blocks
|Inputs per RB||Outputs per RB||Total Rules|
fuzzyTECH supports various fuzzy logic inference methods and algorithms. The following table lists methods of fuzzification and Types of membership functions (MBF) supported by each fuzzyTECH Edition. Standard MBFs are sometimes called "4-point definitions". Arbitrary MBFs can be defined with up to 16 points of definition. Inverse MBFs (inverse terms) are useful for filling a rule part with the negated form of an already existing term. The column MBF Shapes shows the available approximation functions for membership functions. The column Fuzzification Method lists the supported algorithms for the fuzzification step of the fuzzy logic inference. Fuzzy Input indicates that variables are inputted as fuzzy values (i.e. as vectors of membership degrees) instead of crisp values. The standard fuzzification method is computation at run time (MBF Computation). For most target hardware implementations, this is the most efficient approach.
|Membership Functions (MBF)||Type||Shape||Fuzzification Methods|
|Standard MBF||Arbitrary MBF||Inverse MBF||Linear||Spline||Fuzzy Input||MBF Computation||Categorical|
The table below summarizes supported methods of fuzzy logic inference and defuzzification. The fuzzy inference consists of three computational steps: Aggregation, Composition, and Result Aggregation. Different operators can be chosen for aggregation (Input aggregation) and result aggregation. Fuzzy operators used for aggregation (Minimum or Maximum) combine the preconditions of each fuzzy rule. Beside this standard operators, some fuzzyTECH Editions support compensatory operators (Gamma, Min-Avg, Min-Max), that help to compute relations between rules formulated with the logic standard operators AND (Minimum) and NOR (Maximum). The second step of the fuzzy rule inference, the composition, works generally with the PROD-Operator as fixed operator. Standard Rules are rules with a fixed rule weight (Degree of Support = 1.0) that cannot be changed. FAM Rules stands for "Fuzzy Associative Maps" and refers to individually weighted rules (Degree of Support = DoS). The last step of fuzzy inference is the so-called result aggregation. Its MAX operator selects the maximum firing degree of all rules matching to the term. The BSUM operator uses a bounded sum. Thus, all firing degrees are summed by using a bound of one. Note that BSUM result aggregation is different from BSUM MoM and BSUM CoA. The bounds are zero and one.
|Inference & Defuzzification||Aggregation||Composition||Result Aggregation||Defuzzification|
|Min.||Max.||Min-Max||Min-Avg||Gamma||Product||Standard Rules||FAM Rules (DoS)||Max||BSUM||CoM||CoA||MoM||Categorical MoM||Fuzzy Output||Hyper CoM 1)|
1) Only available as add-on module
The result of the fuzzy inference is a fuzzy value that has to be re-transformed into a crisp value. This transformation is called Defuzzification. Different computation methods can be applied for defuzzification. The standard defuzzification is CoM (Center-of-Maximum), delivering the "best compromise" for the inference result. It is equivalent to most implementations of Center-of-Area (CoA) and Center-of-Gravity (CoG) methods. The MoM (Mean-of-Maximum) method delivers the "most plausible" result. Hence, it is mostly used in applications such as pattern recognition, data analysis, and decision support. The defuzzification method HyperCoM (see last column) is only available as add-on module for a few editions. HyperCoM is a defuzzification method that takes both positive and negative experience into consideration (e.g. in the form of recommendations and warnings). A hyperdefuzzification strategy weights these recommendations and warnings against each other and computes a membership function, from which HyperCoM then computes the optimum based output value.
System Optimization and Analysis, Add-on
All fuzzyTECH Editions come with a complete set of debugging features (Debug Modes) and analysis tools Analyzers (see Table below). Remote debugging, where fuzzyTECH running on the PC debugs a fuzzy logic system running on a different computer/microcontroller, is facilitated by Online and RTRCD debug modes. The RTRCD debug mode (Real Time Remote Cross Debugging) lets you analyze the running system and modify rules and membership functions. The Online debug mode in addition allows for all types of modifications on-the-fly. To expand the capabilities of fuzzyTECH, several Add-on Modules are available. The HyperInference Module expands traditional fuzzy logic rule inference with the ability of "prohibitive" rules. Click on the [Add-On Modules] item in the treeview to your left for more information.
Debug Modes (internal)
|Interactive||File / Batch||DDE||RTRCD||Online||TCP/IP||Serial Interface (RS232)|| User-
3D Plot, Time Plot, Trace
The table below lists supported code generator options. The File Code option generates a complete C source code for an executable program which accepts file data as input and writes outputs to a file as well. Since fuzzyTECH generates the complete fuzzy logic system as a single function, input and output values can be transferred as function parameters (Function Call) over the system stack. The C Code column lists which C compiler standard is supported. Besides, Code Interface Resolution indicates the data types of the interfaces, too.
|Code Generation||Code-Options||C-Code||Java-Code|| ActiveX
fuzzyTECH Runtime DLL
Visual Basic 1)
| Structured Text by
IEC 61131-3 standard
| I/O passing as
|Code Interface Resolution (8 Bit, 16 Bit, double)|
1)Using fuzzyTECH Runtime DLL.