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<p>Hybrid intelligent systems allow the development of robust solutions to help solve complex problems in different application areas, such as pattern recognition or control problems. Human recognition allows us to determine who a person is or if a person is who it claims to be. Using multimodal biometry allows us to be more confident about the identification. In this work, a genetic algorithm is developed to design Interval Type-3 fuzzy systems to integrate modular neural network responses, where each modular neural network recognizes using a specific biometric measure. The design includes the number, type, parameters of membership functions, and fuzzy rules. The effectiveness of the Type-3 fuzzy systems is proved by combining modular neural network responses using images with and without noise, and the results are compared with Interval and General Type-2 fuzzy systems. The results achieved show statistically better results than interval Type-2 fuzzy systems with images without and with noise, whereas, compared with General Type-2 fuzzy systems, the advantages of the interval Type-3 fuzzy systems are observed only when images with noise are simulated in the modular neural networks. Regarding execution time, the proposed method has an average time lower than the Interval and General Type-2 fuzzy systems because it needs fewer fuzzy if–then rules to achieve a better average recognition rate.</p>

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Optimal Genetic Design of Interval Type-3 Fuzzy Aggregators for Modular Neural Networks Applied to Human Recognition

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Daniela Sánchez,
  • Patricia Melin,
  • Oscar Castillo,
  • Juan R. Castro

摘要

Hybrid intelligent systems allow the development of robust solutions to help solve complex problems in different application areas, such as pattern recognition or control problems. Human recognition allows us to determine who a person is or if a person is who it claims to be. Using multimodal biometry allows us to be more confident about the identification. In this work, a genetic algorithm is developed to design Interval Type-3 fuzzy systems to integrate modular neural network responses, where each modular neural network recognizes using a specific biometric measure. The design includes the number, type, parameters of membership functions, and fuzzy rules. The effectiveness of the Type-3 fuzzy systems is proved by combining modular neural network responses using images with and without noise, and the results are compared with Interval and General Type-2 fuzzy systems. The results achieved show statistically better results than interval Type-2 fuzzy systems with images without and with noise, whereas, compared with General Type-2 fuzzy systems, the advantages of the interval Type-3 fuzzy systems are observed only when images with noise are simulated in the modular neural networks. Regarding execution time, the proposed method has an average time lower than the Interval and General Type-2 fuzzy systems because it needs fewer fuzzy if–then rules to achieve a better average recognition rate.