A new optimized GA-RBF neural network algorithm

Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM

With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very common. However, the truck operation caused the 3D point cloud model of the mining area to contain dust points, and the 3D point cloud model established by the Context Capture modeling software is a hollow structure. The previous point cloud denoising algorithms caused holes in the model. In view of the above problems, this paper proposes the point cloud denoising method based on orthogonal total least squares fitting and two-layer extreme learning machine improved by genetic algorithm (GA-TELM). The steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-TELM to repair holes. The advantages of the proposed method are listed as follows. First, this method could denoise without generating holes, which solves engineering problems. Second, GA-TELM has a better effect in repairing holes compared with the other methods considered in this paper. Finally, this method starts from actual problems and could be used in mining areas with the same problems. Experimental results demonstrate that it can remove dust spots in the flat area of the mine effectively and ensure the integrity of the model.

Application of an artificial neural network for the improvement of agricultural drainage water quality using a submerged biofilter

Artificial neural network (ANN) mathematical models, such as the radial basis function neural network (RBFNN), have been used successfully in different environmental engineering applications to provide a reasonable match between the measured and predicted concentrations of certain important parameters. In the current study, two RBFNNs (one conventional and one based on particle swarm optimization (PSO)) are employed to accurately predict the removal of chemical oxygen demand (COD) from polluted water streams using submerged biofilter media (plastic and gravel) under the influence of different variables such as temperature (18.00-28.50 °C), flow rate (272.16-768.96 m³/day), and influent COD (55.50-148.90 ppm). The results of the experimental study showed that the COD removal ratio had the highest value (65%) when two plastic biofilter media were used at the minimum flow rate (272.16 m³/day). The mathematical model results showed that the closeness between the measured and obtained COD removal ratios using the RBFNN indicates that the neural network model is valid and accurate. Additionally, the proposed RBFNN trained with the PSO method helped to reduce the difference between the measured and network outputs, leading to a very small relative error compared with that using the conventional RBFNN. The deviation error between the measured value and the output of the conventional RBFNN varied between + 0.20 and - 0.31. However, using PSO, the deviation error varied between + 0.058 and - 0.070. Consequently, the performance of the proposed PSO model is better than that of the conventional RBFNN model, and it is able to reduce the number of iterations and reach the optimum solution in a shorter time. Thus, the proposed PSO model performed well in predicting the removal ratio of COD to improve the drain water quality. Improving drain water quality could help in reducing the contamination of groundwater which could help in protecting water resources in countries suffering from water scarcity such as Egypt.

Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm

The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization (GWO) algorithm, such as slow convergence speed and easily getting into local optimum, an adaptive division of labor search strategy is proposed, which makes the population have abilities of both large-scale search and local exploration in the entire optimization process. Then, the IGWO algorithm is used to optimize RBFNN, finally, establishing a sea clutter prediction model (IGWO-RBFNN) and realizing the prediction and suppression of sea clutter. Experiments show that the IGWO algorithm has significantly improved convergence speed and optimization accuracy. Compared with the particle swarm algorithm with linear decreasing weight strategy (LDWPSO) and the GWO algorithm, the RBFNN prediction model optimized by the IGWO algorithm has higher prediction accuracy and has a better suppression effect on sea clutter of HFSWR.

A Review of Epidemic Forecasting Using Artificial Neural Networks

Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.

Solving Classification Problems Using Projection-Based Learning Algorithm with Fuzzy Radial Basis Function Neural Network

Radial basis function (RBF) is combined with fuzzy C-means algorithms and its learning process made by projection-based learning (PBL) has been proposed in this paper, which is pointed out as PBL-fuzzy radial basis function (PBL-FRBF). The proposed method PBL-FRBF is producing good performances by selecting appropriate center and its width in order to achieve it by unsupervised classification algorithms instead of random selection. The PBL decreases the learning time, finds optimum output weight by its energy function and prefers smallest amount of samples for testing. Performance analysis is evaluated by benchmark datasets for classification problem taken from the UCI machine learning repository. The performance of the proposed PBL-FRBF has produced superior results when compared with FRBF and RBF for classification problems.