L.T. Kóczy

Generalised Weighted Relevance Aggregation Operators for Hierarchical Fuzzy Signatures

Hierarchical Fuzzy Signatures are generalizations of the Vector Valued Fuzzy Set concept introduced in the 1970s. A crucial question in the Fuzzy Signature context is what kinds of aggregations are applicable for combining data with partly different substructures. Our earlier work introduced the Weighted Relevance Aggregation method to enhance the accuracy of the final results of calculations based on Hierarchical Fuzzy Signature Structures. In this paper, we further generalise the weights and the aggregation into a new operator called Weighted Relevance Aggregation Operator (WRAO). WRAO enhances the adaptability of the fuzzy signature model to different applications and simplifies the learning of fuzzy signature models from data. We also show the methodology of learning these aggregation operators from data.

On The Issue of Learning Weights from Observations for Fuzzy Signatures

We investigate the issue of obtaining weights, which are associated with aggregation in fuzzy signatures, from real world data. Our approach will provide a way to extract the relevance of lower levels to the higher levels of the hierarchical fuzzy signature structure. We also handle the non-differentiability of max-min aggregation functions for gradient based learning. A mathematically proved method, which is found in the literature to approximate the derivatives of max-min functions, has been used.

Sparse fuzzy systems generation and fuzzy rule interpolation: a practical approach

In this paper, we explore the use of a sparse fuzzy system generation technique in conjunction with simple projection-based fuzzy rule interpolation, to generate sparse fuzzy systems with relatively few rules whilst still achieving reasonable system accuracy. Through setting a parameter value, the user is able to control, to some extent, the number of rules generated by the rule extraction technique. The rule interpolation approach enables the sparse fuzzy system to maintain a reasonable accuracy. The effectiveness of this approach is validated experimentally.

Fuzzy rough signatures

We extend the idea of Fuzzy Signature to Fuzzy Rough Signature (FRS). The proposed Fuzzy Rough Signature is capable of handling most kind of uncertainty: epistemic and random uncertainty, vagueness due to indiscernibility, and linguistic vagueness that exists in both large as well as small sample data sets. Additionally, this system is capable of hierarchical organization of inputs and use of flexible aggregation selection will simplify the combinations of inputs from different sources.

Intelligent Robot Cooperation with Fuzzy Communication

Designing the decision-making engine of a robot which works in a collaborative team is a challenging task. This is not only due to the complexity of the environment uncertainty, dynamism and imprecision, but also because of the coordination of the team has to be included in this design. The robots must be aware of other robots’ actions in order to cooperate and to successfully achieve their common goal. In addition, decisions must be made in real-time and using limited computational resources. In this chapter we propose some novel algorithms for action selection in ambiguous tasks where the communication opportunities among the robots are very limited.

Fuzzy Situational Maps: A new approach in mobile robot cooperation

Intelligent robot cooperation tasks have very complex decision-making and computational processes. Collecting and calculating with a high amount of data is one of the weakest point of such system. In addition all of these it is necessary to process in real-time with limited computational capacity. In this paper we propose some novel algorithms for coping with these problems and give some information about the Fuzzy Situational Maps as a special case of the Fuzzy Signatures. An example takes to the field of warehouse logistics, managing and arranging boxes will be presented.

Maintenance of local fuzziness in rule interpolation

Approximate reasoning using fuzzy rule based systems has a wide application in, for example, industrial control, property prediction, and in pattern recognition areas. We introduce our method which is conservative with respect to the degree of local fuzziness in the rule base, and demonstrate its utility on a petroleum engineering problem.