Projectile impact point prediction method based on GRNN

In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.

Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.

Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model

The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.

Quantifying social vulnerability for flood disasters of insurance company

Social vulnerability assessments are largely ignored when compared with biophysical vulnerability assessments. This is mainly due to the fact that there are more difficulties in quantifying them. Aiming at several pitfalls still existing in the Hoovering approach which is widely accepted, a suitable modified model is provided. In this modified model, the integrated vulnerability is made an analogy to the elasticity coefficient of a spring, and an objective evaluation criterion is established. With the evaluation criterion, the assessment indicators of social vulnerability are filtered and their weight assignments are accomplished. There is an application in the city of Changsha where floods occur often. With the relative data from the PICC Hunan Province Branch, a generalized regression neural network model is established in Matlab 7.0 and used to evaluate a company's flood social vulnerability index (SoVI). The results show that the average flood social vulnerability in Yuhua district is the highest, while Yuelu district is the lowest. It is good for disaster risk management and decision-making of insurance companies.

Deep Scalogram Representations for Acoustic Scene Classification

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly,the features extracted from a subsequent fully connected layer are fed into(bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer;finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events(DCASE). On the evaluation set, an accuracy of 64.0 % from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0 % baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy,when fusing with a spectrogram-based system.

A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN

High-precision day-ahead short-term photovoltaic(PV)output forecasting is essential in PV integration to the smart distribution networks and multi-energy system,and provides the foundation for the security,stability,and economic operation of PV systems.This paper proposes a hybrid model based on principal component analysis,grey wolf optimization and generalized regression neural network(PCA-GWO-GRNN)for day-ahead short-term PV output forecasting,considering the features of multiple influencing factors and strong uncertainty.This paper first uses the PCA to reduce the dimension of meteorological features.Then,the high-precision day-ahead short-term PV output forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after dimension reduction,and the parameter of GRNN is optimized by using GWO,which has strong global searching ability and fast convergence.The proposed PCA-GWO-GRNN model effectively achieves a high precision in day-ahead shortterm PV output forecasting,which is demonstrated in a case study on a real PV plant in Jiangsu province,China.The results have validated the accuracy and applicability of the proposed model in real scenarios.

Identification of Graves’ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

Diagnosis of the Graves’ophthalmology remains a significant challenge.We identified between Graves’ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy(LIBS)combined with machine learning method.In this work,the paraffin-embedded samples of the Graves’ophthalmology were prepared for LIBS spectra acquisition.The metallic elements(Na,K,Al,Ca),non-metallic element(O)and molecular bands((C-N),(C-O))were selected for diagnosing Graves’ophthalmology.The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis(LDA),support vector machine(SVM),k-nearest neighbor(ANN),and generalized regression neural network(GRNN),respectively.The results showed that the predicted accuracy rates of LDA,SVM,ANN,GRNN were 76.33%,96.28%,96.56%,and 96.33%,respectively.The sensitivity of four models were 75.89%,93.78%,96.78%,and 96.67%,respectively.The specificity of four models were 76.78%,98.78%,96.33%,and 96.00%,respectively.This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ophthalmopathy with a higher rate of accuracy.The ANN had the best performance by comparing the three nonlinear models.Therefore,LIBS combined with machine learning method can be an effective way to discriminate Graves’ophthalmology.