This paper presents a Fuzzy Classi er System(FCS) which can discover fuzzy rules e ciently. The system translates human's knowledge into symbolic information, and e ectively limits its search space for the fuzzy rules by utilizing the symbols. The system can also extracts symbolic information from the acquired fuzzy rules for e cient exploration of another new fuzzy rules. Simulations are done ...

We present different techniques of fuzzy rule generation using the information we can obtain from the fuzzy clustering of a set of data which describe the behavior of a given system. The methods all try to obtain a first model of the consisted system that is good enough to serve as a first approximation for inference purposes. Thus, it is important that the methods should be as simple as possib...

designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing fuzzy learning classifier (flc) systems. conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. in thispaper new entities namely precision and recall from the field of information retrieval (ir)systems is adapted as alternative...

In medical data analysis classification combined with rule generation is a common technique to obtain diagnosis results together with a rule based explanation. In this contribution we apply a neural network based rule generator in the domain of septic shock research. The septic shock is of special interest in intensive care medicine due to its high lethality rate. We describe the functionality ...

Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this d...

The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering...

Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem. mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment. i.e., lack ...

In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training eramples by a ...