Flexible Neuro-fuzzy Systems Research Articles

A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects

In this paper we propose a new approach to nonlinear modelling. It uses capabilities ofthe so-called flexible neuro-fuzzy systems and evolutionary algorithms. The aim of our method is not only to achieve appropriate accuracy of the model, but also to ensure the possibility of interpretability of the knowledge within it. The proposed approach was achieved by, among others, appropriate selection of operational criteria applied to evolutionary model creation. It allows to extract interpretable fuzzy rules in the cases which use the learning data e.g. from identification. The possibility of interpretation of knowledge accumulated in the model seems to be important in practice, because it guarantees operation predictability and facilitates production of efficient and accurate control methods. Our method was tested with the use of well-known simulation problems from the literature.

On-line signature verification using vertical signature partitioning

In this paper we propose a new approach to identity verification based on the analysis of the dynamic signature. Considered problem seems to be particularly important in terms of biometrics. Effectiveness of signature verification significantly increases when dynamic characteristics of the signature are considered (e.g. velocity, pen pressure, etc.). These characteristics are individual for each user and difficult to forge. The effectiveness of the verification on the basis of an analysis of the dynamics of the signature can be further improved. A well-known way is to consider the characteristics of the signature in the sections called partitions. In this paper we propose a new method for identity verification which uses partitioning. Partitions represent time moments of signing of the user. In the classification process the partitions, in which the user created more stable reference signatures during acquisition phase, are more important. Other important features of our method are: using capabilities of fuzzy set theory and development on the basis of them the flexible neuro-fuzzy systems and interpretable classification system for final signature classification. In this paper we have included the simulation results for the two currently available databases of dynamic signatures: free SVC2004 and commercial BioSecure database.

On design of flexible neuro-fuzzy systems for nonlinear modelling

In this work, we consider the flexible neuro-fuzzy systems of the Mamdani-type. When designing such systems to solve approximation problem, we should choose triangular norms used in inference and aggregation operators. This can be done by trial and error. In this work, we propose an algorithm that allows in an automatic way to choose the types of triangular norms in the learning process. The task of this algorithm is also an automatic selection of parameters of all functions describing the system. The algorithm uses an evolutionary strategy for its action and has been tested using well-known approximation benchmarks.

On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification

In the paper, the evolutionary strategy is applied for learning flexible neuro-fuzzy systems. In the process of evolution we determine: fuzzy inference (Mamdani type or logical type—described by an S -implication), specific fuzzy implication, if the logical type system is found in the process of evolution or specific t -norm connecting antecedents and consequences, if the Mamdani type system is found in the process of evolution, specific t -norm for aggregation of antecedents in each rule, specific triangular norm describing aggregation operator, shapes and parameters of fuzzy membership functions, weights describing importance of antecedents of rules, and weights describing importance of rules, parameters of adjustable triangular norms, parameters of soft triangular norms. It should be noted that the crossover and mutation operators are chosen in a self-adaptive way. The method is tested using well known classification benchmarks.

Flexible neuro-fuzzy systems

In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty weights to aggregation of rules and to connectives of antecedents; and 3) parameterized families of T-norms and S-norms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neuro-fuzzy systems. Through computer simulations, we show that Mamdani-type systems are more suitable to approximation problems, whereas logical-type systems may be preferred for classification problems.