Type-1 and Type-2 Fuzzy Parameter Adaptation in PSO for CNN Architecture Optimization Applied to Facial Emotion Recognition
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i4.1486Keywords:
artificial neural networks, Convolutional neural network, Particle Swarm Optimization, fuzzy logicAbstract
It is known that through the use of Convolutional Neural Networks (CNNs) classification problems can be solved, such as facial emotion classification. Fuzzy logic has allowed to reduce the uncertainty that may exist when making a decision. In our work, a convolutional neural network model is proposed that allows, through a fuzzy Mamdani inference system, to choose the optimal hyperparameters within Particle Swarm Optimization (PSO), adjusting variables such as inertial weight and cognitive and social acceleration coefficients, in order to determine the number of convolutional layers and filters to be used within the CNN model. We evaluate Type-1 and interval Type-2 fuzzy adaptation within an identical search space and training setup, focusing on how the treatment of uncertainty shapes the quality of the resulting architectures. The experimental results allow an evaluation of the proposed hybrid system for facial emotion classification using the FER2013 dataset.
Spanish-language metadata / Metadatos en español
Título en español:
Adaptación difusa de parámetros de tipo 1 y tipo 2 en PSO para la optimización de arquitecturas CNN aplicada al reconocimiento de emociones faciales
Resumen:
Es bien sabido que las redes neuronales convolucionales (CNN) permiten resolver problemas de clasificación, como la clasificación de emociones faciales. La lógica difusa ha contribuido a reducir la incertidumbre que puede existir durante la toma de decisiones. En este trabajo se propone un modelo de red neuronal convolucional que, mediante un sistema de inferencia difusa de Mamdani, permite seleccionar los hiperparámetros óptimos dentro de la optimización por enjambre de partículas (PSO). Para ello, se ajustan variables como el peso de inercia y los coeficientes de aceleración cognitiva y social, con el propósito de determinar el número de capas convolucionales y filtros que deben utilizarse en el modelo CNN. Se evalúan la adaptación difusa de tipo 1 y la adaptación difusa de tipo 2 por intervalos dentro de un espacio de búsqueda y una configuración de entrenamiento idénticos, prestando especial atención a la manera en que el tratamiento de la incertidumbre influye en la calidad de las arquitecturas resultantes. Los resultados experimentales permiten evaluar el sistema híbrido propuesto para la clasificación de emociones faciales mediante el conjunto de datos FER2013.
Palabras Claves:
Redes neuronales artificiales.
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