RFM model is an important method in customer clustering. Some past studies have proposed fuzzy RFMs model to overcome the shortcomings of traditional RFM models. However, there are some problems unsolved in these approaches. To deal with these problems and to enhance the flexibility in customer clustering, the traditional fuzzy c-means (FCM) method is fuzzified to deal with R, F, and M scores t...

This paper proposes a new methodology for designing Fuzzy Cognitive Maps using crisp decision trees that have been fuzzified. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from...

Fuzzy clustering algorithms like the popular fuzzy cmeans algorithm (FCM) are frequently used to automatically divide up the data space into fuzzy granules (fuzzy vector quantization). In the context of fuzzy systems, in order to be intuitive and meaningful to the user, the fuzzy membership functions of the used linguistic terms have to fulfill some requirements like boundedness of support or u...

Fuzzy C-mean (FCM) is the most well-known and widely-used fuzzy clustering algorithm. However, one of the weaknesses of the FCM is the way it assigns membership degrees to data which is based on the distance to the cluster centers. Unfortunately, the membership degrees are determined without considering the shape and density of the clusters. In this paper, we propose an algorithm which takes th...

Introduction Knowledge management (KM) and artificial intelligence (AI) are interconnected disciplines to discern information for information management systems. Researchers have raised issues of knowledge that are living and active. Decisions based on real life knowledge bases are subjective judgments in nature. AI has well-developed cognitive tools that can process qualitative information of ...

Artificial potential fields(APF) are well established for reactive navigation of mobile robots. This paper describes a fast and robust fuzzy-APF on an ActivMedia AmigoBot.Obstacle-related information is fuzzified by using sensory fusion, which results in a shorter runtime. In addition, the membership functions of obstacle direction and range have been merged into one function, obtaining a small...

This paper proposes an approach to derive fuzzy granules from numerical data. Granules are first formed by means of a doubleclustering technique, and then properly fuzzified so as to obtain interpretable granules, in the sense that they can be described by linguistic labels. The double-clustering technique involves two steps. First, information granules are induced in the space of numerical dat...

From a conventional mathematical programming model, and in accordance with which fuzzification is used, several models of fuzzy mathematical programming problems can be obtained. This paper deals with the study of the optimality concept for (g,p)-fuzzified mathematical programming problems. An auxiliary parametric mathematical programming problem is presented which allows the above model to be ...

The inversion of a neural network is a process of computing inputs that produce a given target when fed into the neural network. The inversion algorithm of crisp neural networks is based on the gradient descent search in which a candidate inverse is iteratively refined to decrease the error between its output and the target. In this paper, we derive an inversion algorithm of fuzzified neural ne...