This paper presents a hybrid probabilistic neural network (PNN) and particle swarm optimization (PSO) techniques to predict the soil liquefaction. The PSO algorithm is employed in selecting the optimal smoothing parameter of the PNN to improve the forecasting accuracy. Seven parameters such as earthquake magnitude, normalized peak horizontal acceleration at ground surface, standard penetration number, penetration resistance, relative compaction, mean grain diameter and groundwater table are selected as the evaluating indices. The predictions from the PSO-PNN model were compared with those from two models: backpropagation neural network (BPNN) model and support vector machine (SVM) model. The study concluded that the proposed PSO-PNN model can be used as a reliable approach for predicting soil liquefaction.
When dealing with the curse of dimensionality (small sample size with many dimensions), feature selection is an important preprocessing strategy for the analysis of biomedical data. This issue is particularly germane to the classification of high-dimensional class-labeled biomedical spectra as is often acquired from magnetic resonance and infrared spectrometers. A technique is presented that stochastically selects feature subsets with varying cardinality for automated discrimination using two types of neural network classifiers. The results are benchmarked against classifiers using the entire feature set with and without averaging. Stochastic feature subset selection had significantly fewer misclassifications than either of the benchmarks.