This paper explores the learning fuzzy inference systems implemented as adaptive fuzzy-neural networks. The research into application of learning techniques to fuzzy inference systems (FIS) has matured into a family of adaptive fuzzy inference systems (AFIS). In most cases, the learning FIS and AFIS families can be interpreted as a partially connected multilayer feedforward neural network with ...

The single input rule modules (SIRMs) based type-1 fuzzy inference systems (SIRM-T1FISs), which assign each input item with a rule module, can greatly reduce the number of fuzzy rules and have found lots of applications. This paper extends SIRMT1FISs to the interval type-2 case to achieve better performance. Furthermore, the properties of the SIRMs based interval type-2 fuzzy inference systems ...

Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, na...

This research suggests a new type of inte1,ligent system based on new approaches to generation and selection of family of fuzzy logics. It is based on new generalizedl strategy of designing of fuzzy intelligent systems. Family of fuzzy logics can be generated by T-norms axiomatic system to choose the logic, that is the most suitable to expert way of thinking. Then the selected fuzzy logic is te...

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work. In this paper, we propose a general learning method as a framework for automatically deriving membership functions and fuzzy if-then rules from a set of given training examples to rapidly build a protot...

This article presents a study on the use of parametrized operators in the Inference System of linguistic fuzzy systems adapted by evolutionary algorithms, for achieving better cooperation among fuzzy rules. This approach produces a kind of rule cooperation by means of the inference system, increasing the accuracy of the fuzzy system without losing its interpretability. We study the different al...

Fuzzy inference has numerous applications, ranging from control to forecasting. A number of researchers have suggested how such systems can be tuned during application to enhance inference performance. Inference parameters that can be tuned include the central tendency and dispersion of the input and output fuzzy membership functions, the rule base, the cardinality of the fuzzy membership funct...

An adaptive fuzzy PID controller with gain scheduling is proposed in this paper. The structure of the proposed gain scheduled fuzzy PID (GS FPID) controller consists of both fuzzy PI-like controller and fuzzy PD-like controller. Both of fuzzy PIlike and PD-like controllers are weighted through adaptive gain scheduling, which are also determined by fuzzy logic inference. A modified genetic algor...