Products/fuzzyTECH Editions/Editions Overview

We have 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. To quickly find the right fuzzyTECH Edition for you needs, refer to the FAQ page.

General Purpose fuzzyTECH Edition

fuzzyTECH IA Editions for Industrial Automation

The fuzzyTECH IA Editions are optimized for Programmable Logic Controllers (PLCs). These Editions generate optimized function blocks for these controllers.  All fuzzyTECH IA Editions feature Online functionality. Please call INFORM if you use a PLC or process controller that is not listed here. Integration with common process control software packages is provided by the fuzzyTECH Professional Edition.

Technical Specifications

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.

Overview Variables Rules
Total Input Output Terms per Variable
Total Terms Rule Blocks
Inputs per RB Outputs per RB Total Rules
Professional 255 255 32 32/255 65535 50 250 11 -
IA-S7 255 255 32 8/- 255 50 250 11 -

Membership Functions
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
Professional x x x x x x x x
IA-S7 x - x x - - x -

Inference and Defuzzification
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)
Professional x x x x x x x x x x x x x x x x
IA-S7 x - - - - x x x x x x - x - - -

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 Modules
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)

Communication Channels Analyzer

Add-on Modules



Interactive File / Batch DDE RTRCD Online TCP/IP Serial Interface (RS232) User-
defined (FTOCC)
3D Plot, Time Plot,  Trace
Hyper Inference
Professional x x x - x x x x x x
IA-S7 x x x x1) - x2) x2) x x -

1) Limited RTRCD / Trace Function, 2)Using LIBNODAVE

Code Generation
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 Hardware
specific Code
C-Code Java-Code ActiveX
fuzzyTECH Runtime DLL
Delphi 1)
Visual Basic 1)
C# 1)
Structured Text by
IEC 61131-3 standard
for CoDeSys



I/O passing as
function parameter
  AWL     ANSI    Java FTR ST
Code Interface Resolution (8 Bit, 16 Bit, double)
Professional x - 8/16/d 16/d 16/d 16/d
IA-S7 x 15 - - - -

1)Using fuzzyTECH Runtime DLL.