A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling

Islas, Marco Antonio; Rubio, José de Jesús; Muñiz, Samantha; Ochoa, Genaro; Pacheco, Jaime; Meda-Campaña, J. A.; Mújica-Vargas, Dante; Aguilar-Ibáñez, Carlos; Gutiérrez, Guadalupe Juliana; Zacarías, Alejandro

doi:10.3390/electronics10040448

Cited by 27 publications

(16 citation statements)

References 39 publications

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“…Velikovi et al [39] propose a deep graph infomax (DGI) that is based upon mutual information to learn graph embeddings on both transduc-tive and inductive learning tasks. By combining these works of [9,17,18,32,33,35] with DGI, DGI is more powerful. Several researches obtain multi-scale information via high-order propagation matrix [2,3,24,29,31].…”

Section: Related Workmentioning

confidence: 99%

“…Our convolution model is used to learn k-hops nodes features and global graph structure by aggregating neighborhoods information as described in Eq. (17). In GCNs, the convolution suffers from inability in capturing nodes feature with different neighbors.…”

Section: Theorem 1 Multi-scale Adjacency Matrix With Attention Mechanism Is An Operation Of Permutation Invariancementioning

confidence: 99%

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AdjMix: simplifying and attending graph convolutional networks

Liu

Lei

Xia

et al. 2021

Complex Intell. Syst.

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Simple graph convolution (SGC) achieves competitive classification accuracy to graph convolutional networks (GCNs) in various tasks while being computationally more efficient and fitting fewer parameters. However, the width of SGC is narrow due to the over-smoothing of SGC with higher power, which limits the learning ability of graph representations. Here, we propose AdjMix, a simple and attentional graph convolutional model, that is scalable to wider structure and captures more nodes features information, by simultaneously mixing the adjacency matrices of different powers. We point out that the key factor of over-smoothing is the mismatched weights of adjacency matrices, and design AdjMix to address the over-smoothing of SGC and GCNs by adjusting the weights to matching values. Experiments on citation networks including Pubmed, Citeseer, and Cora show that our AdjMix improves over SGC by 2.4%, 2.2%, and 3.2%, respectively, while achieving same performance in terms of parameters and complexity, and obtains better performance in terms of classification accuracy, parameters, and complexity, compared to other baselines.

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Section: Related Workmentioning

confidence: 99%

Section: Theorem 1 Multi-scale Adjacency Matrix With Attention Mechanism Is An Operation Of Permutation Invariancementioning

confidence: 99%

AdjMix: simplifying and attending graph convolutional networks

Liu

Lei

Xia

et al. 2021

Complex Intell. Syst.

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show abstract

“…Twenty pictures were tested, and the accuracy rate was 90.8% based on the number of pictures [11]. With the continuous development of research, deep learning technology has become a hot spot in the research of image recognition like [12][13][14][15][16], including apple fruit targets in natural scenes due to its high detection accuracy. Jia et al, fused a K-means clustering algorithm and GA-RBF-LMS neural network to identify and detect 179 apple fruits, and the accuracy rate calculated by the number of apples was 96.95% [17].…”

Section: Introductionmentioning

confidence: 99%

On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

Wang

Han

et al. 2021

Applied Sciences

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For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an important impact on the subsequent edge prediction, this paper proposed an automatic detection method for apple fruit targets in natural environments based on saliency detection and traditional color difference methods. Combined with the original image, the histogram backprojection algorithm was used to further optimize the salient image results. A dynamic adaptive overlapping target separation algorithm was proposed to locate the single target fruit and further to determine the initial contour for DeepSnake, in view of the possible overlapping fruit regions in the saliency map. Finally, the target fruit was labeled based on the segmentation results of the examples. In the experiment, 300 training datasets were used to train the DeepSnake model, and the self-built dataset containing 1036 pictures of apples in various situations under natural environment was tested. The detection accuracy of target fruits under non-overlapping shaded fruits, overlapping fruits, shaded branches and leaves, and poor illumination conditions were 99.12%, 94.78%, 90.71%, and 94.46% respectively. The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images, which showed that the proposed algorithm can effectively separate the overlapping fruits through a not-very-large training samples and realize the rapid and accurate detection of apple targets.

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“…For nonlinear systems whose models are completely unknown, the function approximation theory of neural networks makes it an effective approximation tool. e kernel function is the core of the neural network [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions

Ai-yu

Han

et al. 2021

Complexity

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With the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter. The high-degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm.

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A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling

Cited by 27 publications

References 39 publications

AdjMix: simplifying and attending graph convolutional networks

AdjMix: simplifying and attending graph convolutional networks

On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions

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