Particle Swarm Optimization Training Algorithm Research Articles

Ionosphere tomography using wavelet neural network and particle swarm optimization training algorithm in Iranian case study

Computerized tomography provides valuable information for imaging the ionospheric electron density distribution. We use a wavelet neural network with a particle swarm optimization training algorithm to solve pixel-based ionospheric tomography. This new method is called ionospheric tomography based on the neural network (ITNN). In this method, vertical and horizontal objective functions are minimized. Due to a poor vertical resolution of ionospheric tomography, empirical orthogonal functions are used as vertical objective function. For numerical experimentation, observations collected at 38 GPS stations on 2 days in 2007 (April 3 and July 13) from the Iranian permanent GPS network (IPGN) are used. Ionosonde observations (ź = 35.7382°, ź = 51.3851°) are used for validating the reliability of the proposed method. The modeling region is between 24°E to 40°E and 44°N to 64°N. The results of the ITNN method have been compared to those of the international reference ionosphere model 2012 (IRI-2012) and the spherical cap harmonics (SCHs) method as a local model. The minimum relative error for ITNN is 1.41% and the maximum relative error is 24.03%. Also, the root-mean-square error of 0.1932 × 1011 (el/m3) has been computed for ITNN, which is less than the RMSE of the IRI-2012 and SCHs method. The comparison of ITNN results with IRI-2012 and SCHs method shows that the proposed approach is superior to those of the traditional methods.

Wavelet neural networks using particle swarm optimization training in modeling regional ionospheric total electron content

Wavelet neural networks (WNNs) are a new class of neural networks (NNs) that has been developed using a combined method of multi-layer artificial neural networks and wavelet analysis (WA). In this paper, WNNs is used for modeling and prediction of total electron content (TEC) of ionosphere with high spatial and temporal resolution. Generally, back-propagation (BP) algorithm is used to train the neural network. While this algorithm proves to be very effective and robust in training many types of network structures, it suffers from certain disadvantages such as easy entrapment in a local minimum and slow convergence. To improve the performance of WNN in training step, the adjustment of network weights using particle swarm optimization (PSO) was proposed. The results obtained in this paper were compared with standard NN (SNN) by BP training algorithm (SNN-BP), SNN by PSO training algorithm (SNN-PSO) and WNN by BP training algorithm (WNN-BP). For numerical experiments, observations collected at 36 GPS stations in 5 days of 2012 from Iranian permanent GPS network (IPGN) are used. The average minimum relative errors in 5 test stations for WNN-PSO, WNN-BP, SNN-BP and SNN-PSO compared with GPS TEC are 10.59%, 12.85%, 13.18%, 13.75% and average maximum relative errors are 14.70%, 17.30%, 18.53% and 20.83%, respectively. Comparison of diurnal predicted TEC values from the WNN-PSO, SNN-BP, SNN-PSO and WNN-BP models with GPS TEC revealed that the WNN-PSO provides more accurate predictions than the other methods in the test area.

Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach

For the design and development of new processes of gas sweetening using ionic liquids (ILs), as promising candidates for amine solutions, an amazing model to predict the solubility of acid gases is of great importance. In this direction, in the current study, the capability of artificial neural networks (ANNs) trained with back propagation (BP) and particle swarm optimization (PSO), to correlate the solubility of H2S in 11different ILs have been investigated. Different structures of three-layer feed forward neural network using acentric factor (ω), critical temperature (Tc), critical pressure (Pc) of ILs accompanied by pressure (P) and temperature (T), as input parameters, were examined and an optimized architecture has been proposed as 5–9–1.Implementation of these models for 465 experimental data points collected from the literature shows coefficient of determination (R2) of 0.99218 and mean squared error (MSE) of 0.00025 from experimental values for PSO-ANN predicted solubilities while the values of R2=0.95151 and MSE=0.00335 were obtained for BP-ANN model. Therefore, through PSO training algorithm we are able to attain significantly better results than with BP training procedure based on the statistical criteria.

Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River

An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks (ANN) has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks such as very slow convergence and easy entrapment in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is applied to predict water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown that the PSO technique can act as an alternative training algorithm for ANNs.