Estimation of rice-planted area using competitive neural network

Omatu, Sigeru

doi:10.1109/ascc.2015.7244759

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…In other applications, the CompNet model based on Competitive Neural Networks and Learnable Gabor Kernels is presented in [21], the model is implemented for Palmprint Recognition; according to the results, the proposal achieved the lowest error rate compared to the most commonly state of the art methods. In [22], a Competitive Neural Network is used to estimate a rice-plant area; the authors demonstrate that these kinds of models are useful for the classification of the satellite data. 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.…”

Section: 𝑑(𝑥 𝑤mentioning

confidence: 99%

Modeling of Fuzzy Systems Based on the Competitive Neural Network

Barraza,

Melin,

Valdez

et al. 2023

Applied Sciences

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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.

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Section: 𝑑(𝑥 𝑤mentioning

confidence: 99%