Optimal Number of Clusters Finding Using the Fireworks Algorithm

Barraza, Juan; Valdez, Fevrier; Melín, Patricia; González, Claudia I.

doi:10.1007/978-3-030-34135-0_7

Cited by 5 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To model the FIS, we selected the Mamdani and Sugeno systems; these have yielded good results in classification problems as we have previously mentioned. The choice of Triangular, Gaussian, and Trapezoidal MFs is based on the experts in the area according to previous works [24,33], where the FIS is modeled with the aforementioned MFs and after several tests, we can obtain the best accuracy results. Finally, it is important to mention that these three types of membership functions are designed with mathematical and metric functions according to their form.…”

Section: Discussionmentioning

confidence: 99%

“…In hybrid methods, in [23], the CNN is optimized and is applied to solve a complex problem in the field of chemical engineering; the authors proposed a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. In [24], the Fireworks Algorithm (FWA) was implemented to optimize the neurons of the CNN and improve the results of the traditional model. In order to improve the CNN, a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing is presented in [25].…”

Section: 𝑑(𝑥 𝑤mentioning

confidence: 99%

See 1 more Smart Citation

Modeling of Fuzzy Systems Based on the Competitive Neural Network

Barraza,

Melin,

Valdez

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.

show abstract

Section: Discussionmentioning

confidence: 99%