Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

Lee, Anzy; Geem, Zong Woo; Suh, Kyung-Duck

doi:10.3390/app6060164

Cited by 54 publications

(26 citation statements)

References 35 publications

(38 reference statements)

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“…The root mean square errors (RMSE) vary from 8.8 cm to 14.1 cm. This suggests that the relationships identified by the networks show sensitive dependence on initially assigned weight and bias for the neurons in the network, which is also indicated by previous studies [62]. In order to find a robust network with high correlation between the retrieved and observed snow depth, we followed an approach widely used in the climate modeling community, ensemble averaging.…”

Section: Ensemble-based Deep Neural Networkmentioning

confidence: 69%

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

et al. 2019

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The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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Section: Ensemble-based Deep Neural Networkmentioning

confidence: 69%

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

et al. 2019

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“…A major contribution of HSA is its capability of representing complex system and stochastic technique. Studies by many researchers suggest the efficiency of HSA compared to other traditional mathematic techniques and heuristic approaches [14][15][16]. Their role is notable when applied to uncertain and complex system comparing to traditional mathematical methods, especially when the aim is determines a random or stochastic process.…”

Section: Harmony Search (Hs) Algorithmmentioning

confidence: 99%

Risk Assessment of Geological Hazards in a Tunneling Project Using Harmony Search Algorithm (Case Study: Ardabil-Mianeh Railway Tunnel)

et al. 2016

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During the design and implementation of underground constructions, the risk assessment and management of geological hazards are important due to the hazards such as the water inflow, collision with crushed fault zones, squeezing and instability around excavation zones. In the present research, it is attempted to study and assess the risk of geological hazards in 378+021 km of the route of Ardabil-Mianeh railway tunnel using the harmony search algorithm (HSA). In the first section of research, after studying structural and geological characteristics during 24 sections of the excavation route, the risk relating to geological hazards including the tunnel instability, squeezing, water inflow and swelling was assessed in three separate classes using HSA. In order to study the accuracy of results, geological hazards recorded during the implementation of excavation operations were used. Studies obtained from the comparison of observed and predicted results indicate the high accuracy of HSA in the assessment and prediction of geological risks in the tunnelling project.

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“…In most studies employing ANN models for the estimation of concrete properties, a BP algorithm was used to train the network [19,20,21]. Nevertheless, the BP algorithm has some disadvantages: it can be easily trapped in local minima depending on the selection of initial parameters and it may be unreliable (with a low prediction accuracy), relying on training data [23,24]. Combinations of BP and several metaheuristic algorithms have been proposed as alternatives to overcome these drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

et al. 2019

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A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

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Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

Cited by 54 publications

References 35 publications

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Risk Assessment of Geological Hazards in a Tunneling Project Using Harmony Search Algorithm (Case Study: Ardabil-Mianeh Railway Tunnel)

Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

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