STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks

Soodi, Hamed Atyia; Vural, Ahmet Mete

doi:10.1155/2018/6381610

Cited by 20 publications

(12 citation statements)

References 56 publications

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“…To counteract the aforementioned limitation of gradient-based algorithms, metaheuristics as a global searching method have been employed to improve the ANN training phase. Various metaheuristic algorithms, such as cuckoo search optimization [ 41 ], bat optimization [ 42 ], monarch butterfly optimization [ 43 ], shuffled frog leap algorithm [ 44 ], kidney-inspired algorithm [ 45 ], and an improved particle swarm optimization [ 46 ], have been recently proposed and investigated. Previous studies show improved performances of metaheuristic-assisted models compared to the traditional models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

Ngo

Hoang

Pradhan

et al. 2018

Sensors

118

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Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.

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Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

Ngo

Hoang

Pradhan

et al. 2018

Sensors

118

View full text Add to dashboard Cite

show abstract

“…On the other hand, the BP method is the primary method of ANNs; however, this method is prone to get stuck in the local minima and also experiences slower concentration rate toward the optimum solution. 38 PSO performs better than BP and GA in terms of rate of concentration. 39,40 According to the above results, we compared our proposed "CLAHE+MGLCM+PSONN" method with the state-of-the-art approaches: NBC, 9 WN+SVM, 10 ELM, 11 and CLAHE+ELM.…”

Section: Discussionmentioning

confidence: 94%

“…Instead, PSO uses a simple formula to update the positions of each particle. On the other hand, the BP method is the primary method of ANNs; however, this method is prone to get stuck in the local minima and also experiences slower concentration rate toward the optimum solution . PSO performs better than BP and GA in terms of rate of concentration …”

Section: Discussionmentioning

confidence: 99%

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Wen

Jiang

Sun

et al. 2019

Int J Imaging Syst Tech

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The oral maintenance of patients with periodontal disease mainly depends on clinical examination. However, insufficient number of medical workers cannot carry out detailed oral health education for a large number of patients within limited time and provide these patients with proper and effective oral health nursing methods. In this research, our study put forward a new Artificial Intelligence (AI) based method to diagnose chronic gingivitis, which is based on multichannel gray-levelco-occurrence matrix (MGLCM) and particle swarm optimization neural network(PSONN). Meanwhile, different training algorithms were used as comparison groups.The data set contains 800 images: 400 chronic gingivitis images and 400 healthy gingiva images. The results certify that the specificity, sensitivity, precision, accuracy and F1 Score of MGLCM (PSONN as a classifier) method is 78.17%, 78.23%, 78.24% ,78.20%and 78.17%, respectively. The association of MGLCM and PSONN is more accurate and efficient than approaches: NBC, WN+SVM,ELM and CLAHE+ELM. K E Y W O R D Sartificial neural network, gingivitis identification, multichannel gray-level co-occurrence matrix, particle swarm optimization, pattern recognition

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“…After the pre-training stage, the resulting network is fine-tuned using the BP algorithm to reach the global optimum [20]. We take the sum of square errors of outputs as the loss function defined as…”

Section: B Dbn Trainingmentioning

confidence: 99%

Bad Data Detection and Identification of Hybrid AC/DC Power Systems with Voltage Source Converters Using Deep Belief Network and K-Means Clustering

Zhang¹,

Yu²,

Zhai³

et al. 2019

ElAEE

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The widespread use of Voltage Source Converter based High Voltage Direct Current Transmission (VSC-HVDC) technology has significantly increased the complexity of grid configuration and operation, which demands higher quality of AC/DC state estimation. The bad data detection and identification plays a vital role in ensuring the accuracy of state estimation outcomes. This paper presents a new approach to bad data in hybrid AC/DC grids based on the combined deep belief network (DBN) and K-means clustering method. First, the DBNs are trained separately for active and reactive power given the characteristics of VSC-HVDC by which the bad data can be detected. Then, an improved K-means is used for clustering the DBN outputs by setting the mean and the number of the samples within the clusters as two metrics for bad data identification. Finally, the case study is performed in a modified IEEE 14-bus system and the results demonstrate the effectiveness of the proposed method in terms of both the accuracy and efficiency.

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STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks

Cited by 20 publications

References 56 publications

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Bad Data Detection and Identification of Hybrid AC/DC Power Systems with Voltage Source Converters Using Deep Belief Network and K-Means Clustering

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