Performance Prediction of Switched Reluctance Motor using Improved Generalized Regression Neural Networks for Design Optimization

Since practical mathematical model for the design optimization of switched reluctance motor(SRM)is difficult to derive because of the strong nonlinearity,precise prediction of electromagnetic characteristics is of great importance during the optimization procedure.In this paper,an improved generalized regression neural network(GRNN)optimized by fruit fly optimization algorithm(FOA)is proposed for the modeling of SRM that represent the relationship of torque ripple and efficiency with the optimization variables,stator pole arc,rotor pole arc and rotor yoke height.Finite element parametric analysis technology is used to obtain the sample data for GRNN training and verification.Comprehensive comparisons and analysis among back propagation neural network(BPNN),radial basis function neural network(RBFNN),extreme learning machine(ELM)and GRNN is made to test the effectiveness and superiority of FOA-GRNN.

An Approach to Carbon Emissions Prediction Using Generalized Regression Neural Network Improved by Genetic Algorithm

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

Stability and Stabilization on Generalized Delay Stochastic Neural Network with Distributed Parameter

Inner stability and stabilization of Cohen-Grossberg generalized delay stochastic neural network with distributed parameter are discussed. The main method adopted is, combining inequality techniques, to apply Ito differential formula to the constructed average function with respect to spatial variables along the system considered under the integral operator. Some sufficient conditions are given.

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network:a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.

Term Structure of Interest Rates Based on Artificial Neural Network

In light of the nonlinear approaching capability of artificial neural networks （ ANN）, the term structure of interest rates is predicted using The generalized regression neural network （GRNN） and back propagation （BP） neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek＇s model and Cox-Ingersoll-Ross （CIR） model. The comparison reveals that neural network models outperform Vasicek＇s model and CIR model, which are more precise and closer to the real market situation.

Generative Adversarial Networks for Secure Data Transmission in Wireless Network

In this paper,a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios.It is designed further to make their transmission decision that automati-cally adapts to the transmission dynamics to mitigate the launched jamming attacks.The generative adversarial learning neural network(GALNN)or genera-tive dynamic neural network(GDNN)automatically learns with the synthesized training data(training)with a generator and discriminator type neural networks that encompass minimax game theory.The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network(GAN).The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm,which improves the communica-tion reliability in the network.The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates.The results show that the GDNN obtains an increased rate of successful transmis-sion by taking optimal actions to act as a defense mechanism to mislead the jam-mer,where the jammer makes high misclassification errors on transmission dynamics.

Short-term Load Forecasting of Regional Distribution Network Based on Generalized Regression Neural Network Optimized by Grey Wolf Optimization Algorithm

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong randomness.This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features.After that the high-precision short-term丨oad forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast convergence.Finally a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.

Underwater Positioning Based on an Artificial Lateral Line and a Generalized Regression Neural Network

Taking advantage of the lateral line organ, fish can navigate, feed, and avoid predators and obstacles by sensing surrounding flow fields. The lateral line organ provides an important reference for the development of new underwater detection technology. Inspired by the lateral line organ, in this paper, for the sake of localizing the target dipole source in three-dimensional underwater space, an artificial lateral line consisting of nine underwater pressure sensors forming a cross-shaped sensor array is applied. Combined with the method of gener- alized regression neural network, which is suitable for solving nonlinear pattern recognition problems, a corresponding experimental platform has been built to sample data for training the neural network from a 12 cm by 12 cm by 24 cm cuboid space. The experimental results indicate that the cross-shaped artificial lateral line can localize the target dipole source two body-lengths away. The well- performing perceptual distance is below 13 cm away from the sensing array. Moreover, decreasing the data sampling interval and in- creasing the number of sensors utilized can help improve the positioning accuracy.

Prediction of Molecular Conformation Using Deep Generative Neural Networks

The accurate prediction of molecular conformations with high efficiency is crucial in various fields such as materials science,computational chemistry and computer-aided drug design,as the three-dimensional structures accessible at a given condition usually determine the specific physical,chemical,and biological properties of a molecule.Traditional approaches for the conformational sampling of molecules such as molecular dynamics simulations and Markov chain Monte Carlo methods either require an exponentially increasing amount of time as the degree of freedom of the molecule increases or suffer from systematic errors that fail to obtain important conformations,thus presenting significant challenges for conformation sampling with both high efficiency and high accuracy.Recently,deep learning-based generative models have emerged as a promising solution to this problem.These models can generate a large number of molecular conformations in a short time,and their accuracy is comparable and,in some cases,even better than that of current popular open-source and commercial software.This Emerging Topic introduces the recent progresses of using deep learning for predicting molecular conformations and briefly discusses the potential and challenges of this emerging field.

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management.To completely extract the correlated state information of SPNDs,we constructed a twin model based on a generalized regression neural network(GRNN)that represents the common relationships among overall signals.Faulty SPNDs were determined because of the functional concordance of the twin model and real monitoring sys-tems,which calculated the error probability distribution between the model outputs and real values.Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures.A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity.The experimental evaluation of the proposed method showed promising results,with excellent output consistency and high detection accuracy for both single-and multiple-point faulty SPNDs.For unexpected excessive failures,the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.

Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks

With the rapid growth of satellite traffic, the ability to forecast traffic loads becomes vital for improving data transmission efficiency and resource management in satellite networks. To precisely forecast the short-term traffic loads in satellite networks, a forecasting algorithm based on principal component analysis and a generalized regression neural network （PCA-GRNN） is proposed. The PCA-GRNN algorithm exploits the hidden regularity of satellite networks and fully considers both the temporal and spatial correlations of satellite traffic. Specifically, it selects optimal time series of spatio-temporally correlated historical traffic from satellites as forecasting inputs and applies principal component analysis to reduce the input dimensions while preserving the main features of the data. Then, a generalized regression neural network is utilized to perform the final short-term load forecasting based on the obtained principal components. The PCA-GRNN algorithm is evaluated based on real-world traffic traces, and the results show that the PCA-GRNN method achieves a higher forecasting accuracy, has a shorter training time and is more robust than other state-of-the-art algorithms, even for incomplete traffic datasets. Therefore, the PCA- GRNN algorithm can be regarded as a preferred solution for use in real-time traffic forecasting for realistic satellite networks.

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.

Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques

Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary.Methods: This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks.Results: The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error(5.1), mean error(-0.85), and mean square prediction error(29). The accuracy rate of the combined k-nearest neighbors(k-NN) local support vector machines model(i.e. k-nearest neighbors-support vector machine(KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network(0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%.Conclusions: Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.

Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model

After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.

Application and Simulation of GRNN for the Shotcrete Robot

The generalized regression neural network-one kind of RBF neural network, is chosen to construct the inverse-kinematics model for the shotcrete robot which has redundant degree-of-freedom. The inverse-kinematics model of the object is trained by the general learning method. In constructing model process, different partition methods is tried to divide the joint space and different diffusion coefficient value to train the neural network. The influence of the spread coefficient to the approach ability is also studied. The simulation method is adopted to test the performance of the neural network. The simulation result turns out to be satisfactory.

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.