A method for response integration in modular neural networks with type-2 fuzzy logic for biometric systems p. 5 Evolving type-2 fuzzy logic controllers for autonomous mobile robots p. 16 Adaptive type-2 fuzzy logic for intelligent home environment p. 26 Interval type-1 non-singleton type-2 TSK fuzzy logic systems using the hybrid training method RLS-BP p. 36 An efficient computational method to...

This paper focuses on the design of a novel control approach. Its contribution to the existing literature is that in the design of an interval type-2 fuzzy neural system, recursive fuzzy c-means clustering algorithm is used and the designed algorithm is applied in control applications. The center and the standard deviation values of the interval type-2 Gaussian membership functions at the antec...

expert systems can help to build banks customers' credit scoring models. here, selection of key features of the credit scoring is important. also, it is possible to express the features values as fuzzy. the problem is how to improve features selection by genetic algorithm, in way that these features can be employed as input in fuzzy expert system. this paper presents a hybrid credit scorin...

As can be seen from the definition of extended operations on fuzzy numbers, subtraction and division of fuzzy numbers are not the inverse operations to addition and multiplication . Hence, to solve the fuzzy equations or a fuzzy system of linear equations analytically, we must use methods without using inverse operators. In this paper, a novel method to find the solutions in which 0 is not the ...

In this study, trajectory control of the Variable Loaded Servo (VLS) system is performed by using a Fuzzy Logic based Iterative Learning Control (ILC) method. In the study, a Iterative Learning PID (IL-PID) Controller is used as the iterative learning control structure. Also, a fuzzy adjustment mechanism has been added to the control system for specify the initial parameter of the IL-PID contro...

The application of type-2 fuzzy logic to the problem of automated quality control in sound speaker manufacturing is presented in this paper. Traditional quality control has been done by manually checking the quality of sound after production. This manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation. For this reason, by applying type-2 ...

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other rece...

In most Fuzzy System applications the structure of the system is chosen non-systematically by an expert according to his knowledge. Nevertheless the system parameters are rich enough to ensure the desired behavior. In the following, we introduce an automatic learning algorithm by de nition of a 1) well behaved and 2) minimal Fuzzy System. A Genetic Algorithm is used to estimate the Fuzzy System...

This paper presents an Genetically Tuned interval type-2 fuzzy PID controller (GTIT2FPIDC) for the solution Load Frequency Control (LFC) problem in a deregulated power system that operate under deregulation based on the bilateral policy scheme. The interval type-2 fuzzy PID controller (GT-IT2FPIDC) is expected to compensate for the sudden load change, as the most effective countermeasure. In or...

and hosti 013.07.0 Abstract This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs) and fuzzy systems. The controller for each degree of freedom (DOF) consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is ...