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2021-05-03
Zalasiński, Marcin, Cpałka, Krzysztof, Łapa, Krystian.  2020.  An interpretable fuzzy system in the on-line signature scalable verification. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–9.
This paper proposes new original solutions for the use of interpretable flexible fuzzy systems for identity verification based on an on-line signature. Such solutions must be scalable because the verification of the identity of each user must be carried out independently of one another. In addition, a large number of system users limit the possibilities of iterative system learning. An important issue is the ability to interpret the system rules because it explains how the similarity of test signatures to reference signature templates is assessed. In this paper, we propose an approach that meets all of the above requirements and works effectively for the on-line signatures' database used in the simulations.
2018-05-02
Menezes, B. A. M., Wrede, F., Kuchen, H., Neto, F. B. de Lima.  2017.  Parameter selection for swarm intelligence algorithms \#x2014; Case study on parallel implementation of FSS. 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI). :1–6.

Swarm Intelligence (SI) algorithms, such as Fish School Search (FSS), are well known as useful tools that can be used to achieve a good solution in a reasonable amount of time for complex optimization problems. And when problems increase in size and complexity, some increase in population size or number of iterations might be needed in order to achieve a good solution. In extreme cases, the execution time can be huge and other approaches, such as parallel implementations, might help to reduce it. This paper investigates the relation and trade off involving these three aspects in SI algorithms, namely population size, number of iterations, and problem complexity. The results with a parallel implementations of FSS show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior of the results, i.e. increasing the population over a certain threshold only leads to slight improvements.

2015-04-30
Chia-Feng Juang, Chi-Wei Hung, Chia-Hung Hsu.  2014.  Rule-Based Cooperative Continuous Ant Colony Optimization to Improve the Accuracy of Fuzzy System Design. Fuzzy Systems, IEEE Transactions on. 22:723-735.

This paper proposes a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. All of the free parameters in a zero- or first-order Takagi-Sugeno-Kang (TSK) FS are optimized through CCACO. The CCACO algorithm performs optimization through multiple ant colonies, where each ant colony is only responsible for optimizing the free parameters in a single fuzzy rule. The ant colonies cooperate to design a complete FS, with a complete parameter solution vector (encoding a complete FS) that is formed by selecting a subsolution component (encoding a single fuzzy rule) from each colony. Subsolutions in each ant colony are evolved independently using a new continuous ant colony optimization algorithm. In the CCACO, solutions are updated via the techniques of pheromone-based tournament ant path selection, ant wandering operation, and best-ant-attraction refinement. The performance of the CCACO is verified through applications to fuzzy controller and predictor design problems. Comparisons with other population-based optimization algorithms verify the superiority of the CCACO.