Matlab Fuzzy Logic Toolbox
Regenia Junke
Fuzzy Logic Toolbox provides MATLAB functions, apps, and a Simulink block for analyzing, designing, and simulating fuzzy logic systems. The product lets you specify and configure inputs, outputs, membership functions, and rules of type-1 and type-2 fuzzy inference systems.
The toolbox lets you automatically tune membership functions and rules of a fuzzy inference system from data. You can evaluate the designed fuzzy logic systems in MATLAB and Simulink. Additionally, you can use the fuzzy inference system as a support system to explain artificial intelligence (AI)-based black-box models. You can generate standalone executables or C/C++ code and IEC 61131-3 Structured Text to evaluate and implement fuzzy logic systems.
Use the Fuzzy Logic Designer app or command-line functions to interactively design and simulate fuzzy inference systems. Define input and output variables and membership functions. Specify fuzzy if-then rules. Evaluate your fuzzy inference system across multiple input combinations.
Implement Mamdani and Sugeno fuzzy inference systems. Convert from a Mamdani system to a Sugeno system or vice versa, to create and compare multiple designs. Additionally, implement complex fuzzy inference systems as a collection of smaller interconnected fuzzy systems using fuzzy trees.
Create and evaluate interval type-2 fuzzy inference systems with additional membership function uncertainty. Create type-2 Mamdani and Sugeno fuzzy inference systems using the Fuzzy Logic Designer app or using toolbox functions.
Tune membership function parameters and rules of a single fuzzy inference system or of a fuzzy tree using genetic algorithms, particle swarm optimization, and other Global Optimization Toolbox tuning methods. Train Sugeno fuzzy inference systems using neuro-adaptive learning techniques similar to those used for training neural networks.
Find clusters in input/output data using fuzzy c-means or subtractive clustering. Use the resulting cluster information to generate a Sugeno-type fuzzy inference system that models the input/output data behavior.
Evaluate and test the performance of your fuzzy inference system in Simulink using the Fuzzy Logic Controller block. Implement your fuzzy inference system as part of a larger system model in Simulink for system-level simulation and code generation.
Implement your fuzzy inference system in Simulink and generate C/C++ code or IEC61131-3 Structured Text using Simulink Coder or Simulink PLC Coder, respectively. Use MATLAB Coder to generate C/C++ code from fuzzy inference systems implemented in MATLAB. Alternatively, compile your fuzzy inference system as a standalone application using MATLAB Compiler.
Use fuzzy inference systems as support systems to explain the input-output relationships modeled by an AI-based black-box system. Interpret the decision-making process of a black-box model using the explainable rule base of your fuzzy inference system.
In recent years, the number and variety of applications of fuzzy logic have increased significantly. The applications range from consumer products such as cameras, camcorders, washing machines, and microwave ovens to industrial process control, medical instrumentation, decision-support systems, and portfolio selection.
Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multivalued logic. However, in a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects without crisp, clearly defined boundaries. In such cases, membership in a set is a matter of degree. In this perspective, fuzzy logic in its narrow sense is a branch of FL. Even in its more narrow definition, fuzzy logic differs both in concept and substance from traditional multivalued logical systems.
Another basic concept in FL, which plays a central role in most of its applications, is that of a fuzzy if-then rule or, simply, fuzzy rule. Although rule-based systems have a long history of use in Artificial Intelligence (AI), what is missing in such systems is a mechanism for dealing with fuzzy consequents and fuzzy antecedents. In fuzzy logic, this mechanism is provided by the calculus of fuzzy rules. The calculus of fuzzy rules serves as a basis for what might be called the Fuzzy Dependency and Command Language (FDCL). Although FDCL is not used explicitly in the toolbox, it is effectively one of its principal constituents. In most of the applications of fuzzy logic, a fuzzy logic solution is, in reality, a translation of a human solution into FDCL.
A trend that is growing in visibility relates to the use of fuzzy logic in combination with neurocomputing and genetic algorithms. More generally, fuzzy logic, neurocomputing, and genetic algorithms may be viewed as the principal constituents of what might be called soft computing. Unlike the traditional, hard computing, soft computing accommodates the imprecision of the real world. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, and low solution cost. In the future, soft computing could play an increasingly important role in the conception and design of systems whose MIQ (Machine IQ) is much higher than that of systems designed by conventional methods.
Among various combinations of methodologies in soft computing, the one that has highest visibility at this juncture is that of fuzzy logic and neurocomputing, leading to neuro-fuzzy systems. Within fuzzy logic, such systems play a particularly important role in the induction of rules from observations. An effective method developed by Dr. Roger Jang for this purpose is called ANFIS (Adaptive Neuro-Fuzzy Inference System). This method is an important component of the toolbox.
Fuzzy logic approximates human reasoning and does a good job of balancing the tradeoff between precision and significance. For instance, when warning someone of an object falling toward them, being precise about the exact mass and speed is not necessary.
Determining the appropriate amount of tip requires mapping inputs to the appropriate outputs. Between the input and the output, the preceding figure shows a black box that can contain any number of things, such as fuzzy systems, linear systems, expert systems, neural networks, differential equations, or interpolated multidimensional lookup tables.
Everything is imprecise if you look closely enough, but more than that, most things are imprecise even on careful inspection. Fuzzy reasoning builds this understanding into the process rather than tacking it onto the end.
You can create a fuzzy system to match any set of input-output data. This process is made particularly easy by adaptive techniques like Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which are available in Fuzzy Logic Toolbox software.
The basis for fuzzy logic is the basis for human communication. This observation underpins many of the other statements about fuzzy logic. Because fuzzy logic is built on the structures of qualitative description used in everyday language, fuzzy logic is easy to use.
The last statement is perhaps the most important one and deserves more discussion. Natural language, which is used by ordinary people on a daily basis, has been shaped by thousands of years of human history to be convenient and efficient. Sentences written in ordinary language represent a triumph of efficient communication.
Automatically tune the parameters of a fuzzy logic system using optimization methods such as genetic algorithms and particle swarm optimization. For more information, see Tuning Fuzzy Inference Systems.
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