Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy i...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usually termed as a sparse rule base), no rule can ...

This paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers. Here the fuzzy controller is constructed on the basis of a decision table (DT), relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience. If the controller performance is not satisfactory, it has previously been suggested that the rule...

In this paper a linearized Heffron-Philips model of a Single Machine Infinite Bus power system with a Fuzzy Logic Power System Stablizer (PSS) is developed. The designed fuzzy-based PSS adjusts two inputs by appropriately processing of the input angular speed and angular acceleration signal, and provides an efficient damping. The behavior of the SMIB system with & without PSS has been compared/...

Evolving Takagi-Sugeno (eTS) fuzzy models are used to build a computational model for the WasteWater Treatment Plant (WWTP) in a paper mill. The fuzzy rule base is constructed on-line from data using a recursive fuzzy clustering algorithm that develops the model structure and parameters. In order to avoid some redundancy in the fuzzy rule base mechanisms for merging membership functions and sim...

Due to its high performance and comprehensibility, fuzzy modelling is becoming more and more popular in dealing with nonlinear, uncertain and complex systems for tasks such as signal processing, medical diagnosis and financial investment. However, there are no principal routine methods to obtain the optimum fuzzy rule base which is not only compact but also retains high prediction (or classific...

This paper presents an automatic learning algorithm which generates a fuzzy rule based knowledge representation. While learning the membership functions and rules the internal structure of the rule base is also considered. This is done by definition of 1 a complexity cost function and 2 a minimal Fuzzy System. A Genetic Algorithm is used to estimate the Fuzzy Systems which capture a low comlex ...

The proposed type of hierarchical fuzzy systems is based on the rule base hierarchy. A quite new algorithm for designing a hierarchical fuzzy system from a conventional fuzzy system is developed and implemented. The important part is the unification procedure. Redundant intermediate labels are found and subsequently unified. Basic requirements are fulfilled – subsystems of a hierarchical system...

It is well known that a fuzzy rule base is a characterization of a partially given mapping (fuzzy function) between fuzzy universes. For practical applications, it is desirable to interpolate that function in order to compute its values at points (fuzzy or crisp) other than fuzzy sets (nodes) in antecedents of the rule base. Moreover, interpolation requires that in the case of coincidence betwe...