In this paper we propose a learning method of fuzzy if-then rules for pattern classification problems. We assume that each training pattern has a weight that describes its importance. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty of the fuzzy if-then rules are determined from the co...

An extension of the Adaboost algorithm is proposed for obtaining fuzzy rule based classifiers from imprecisely perceived data. Isolated fuzzy rules are regarded as weak learners, and knowledge bases as ensembles. Rules are iteratively added to a base, and the search of the best rule at each iteration is carried out by a genetic algorithm driven by a fuzzy fitness function. The successive weight...

To apply fuzzy logic, two major tasks need to be performed: the derivation of production rules and the determination of membership functions. These tasks are often difficult and time consuming. This paper presents an algorithmic method for generating membership functions and fuzzy production rules; the method includes an entropy minimization for screening analog values. Membership functions are...

In this paper a Cellular Fuzzy Associative Memory containing fuzzy rules for bidimensional image fuzzification in robot vision systems is developed. This cellular processor constitutes a subsystem of a CNNbased architecture which can store both bidimensional patterns and the rules to process them. After establishing the fuzzy rules characterizing the Fuzzy Associative Memory, a CNN behaving as ...

Development of a neural-fuzzy model for an operational hydrocyclone is reported in this paper. The model integrates the benefits of the Artijkial Neural Network (ANN) and the fuzzy-logic techniques. It preserves the generalisation capability of an ANN while expressing the final model in fuzzy rules. These rules can be modiJied and examined by the user. This will in turn control the interpretati...

We use fuzzy logic rules to directly map sensor data to robot control outputs by classifying a set of typi cal subtasks such as path tracking local collision avoidance contour tracking situation evaluation etc With the help of existing heuristics the decision making process for each subtask can be modelled and represented with IF THEN rules The underlying concepts of mapping with fuzzy logic ru...

We had presented fuzzy rule generation methods by Genetic Algorithm. In this paper, we propose three methods to determine rule pairs for crossover in GA for fuzzy rules generation in order to improve search e ciency and reduction of the number of rules. The rst two methods are that rule pairs are determined based on a distance between rules of two individuals to be crossed. The third one is tha...

Once the input values are given, the active rules in a fuzzy inference execution have been determined. Based on the observation, our approach is to identify the active rules before fuzzy inference execution. To achieve this goal, our architecture provides the following two mechanisms: (1) a mechanism to ignore the non-active rules before fuzzy inference execution; and (2) a mechanism to arrange...

Although an overall knowledge discovery process consists of a distinct pre-processing stage followed by the data mining step, it seems that existing formal concept analysis (FCA) and association rules mining (ARM) approaches, dealing with many-valued contexts, mainly focus on the data mining stage. An “intelligent” pre-processing of input contexts is often absent in existing FCA/ARM approaches,...

Fuzzy control systems and neural-network control systems for backing up a simulated truck, and truck-and-trailer, to a loading dock in a parking lot are presented. The supervised backpropagation learning algorithm trained the neural network systems. The robustness of the neural systems was tested by removing random subsets of training data in learning sequences. The neural systems performed wel...