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...

Fuzzy systems working with classical compositional rule based reasoning methods require the existence of applicable rules for all the possible observations, i.e. an ε>0 covering rule base. In several cases this demand cannot be fulfilled either due to lack of proper rules or the limited storing and processing possibilities. In such circumstances the application of FRI based inference techniques...

Some difficulties emerging during the construction of fuzzy rule bases are inherited from the type of the applied fuzzy reasoning. In fuzzy systems, when classical methods (e.g. the Compositional Rule of Inference) are applied, the completeness of the fuzzy rule base is required to generate meaningful output. This means, that the fuzzy rule base has to cover all possible inputs. The way of buil...

Fuzzy classification has become very necessary because of its ability to use simple linguistically interpretable rules and has get control over the limitations of symbolic or crisp rule based classifiers. This paper mainly deals with classification on the basis of soft computing techniques Fuzzy cognitive maps and fuzzy inference system. But the data available for classification contain some mi...

in this study, the roll, yaw and depth fuzzy control of an au- tonomous underwater vehicle (auv) are addressed. yaw and roll angles are regulated only using their errors and rates, but due to the complexity of depth dynamic channel, additional pitch rate quantity is used to improve the depth loop performance. the discussed auv has four aps at the rear of the vehicle as actuators. two rule bases...

This paper aims at proposing a framework for modelling the safety of an engineering system with various types of uncertainties using a fuzzy rule-based evidential reasoning (FURBER) approach. In the framework, parameters used to define the safety level, including failure rate, failure consequence severity and failure consequence probability, are described using fuzzy linguistic variables; a fuz...

This paper proposes a new hybrid time series forecasting system which is the fusion of the fuzzy system and the artificial neural network. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the inference layer, the hidden layer, and the output layer. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is ...

A neural fuzzy system learning with linguistic teaching signals is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. At first, we propose a fivelayered neural network for the connectionist realization of a fuzzy inference system. Th...

In this paper, an Integrated C-Object Tool, namely ICOT, is proposed for knowledge-based programming. A major drawback of current rule-based expert system languages is that they have difficulty in handling composite objects as a unit of inference. An object-oriented model is a powerful alternative to complement the drawback. Each of these alone cannot capture all the semantics of knowledge, par...

We report on experiments designed to highlight the strengths and weaknesses of an autonomous rule acquisition algorithm for the fuzzy controller of a simulated mobile robot. The algorithm is a Pittsburghstyle Fuzzy Classifier System. The highly cross-coupled and co-operative nature of fuzzy inference systems makes autonomous creation of an optimal rule-base a tough proposition. However, our res...