普陀山岛旅游生态安全发展趋势预测

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

This article introduces the design and implementation of a fishery forecasting system based on Radial Basis Function (RBF) neural network. The system was developed using the CLient/Server architecture, the C# programming language in the environment of Visual Studio 2008 on the Windows7 platform. It draws knowledge from RBF neural network theory, the production historical data of pelagic fishery and the marine environment data. The system uses the Object-Oriented analysis and design method. It can quickly obtain the forecast results available to users through inputting marine environment data information and the RBF neural network model. The forecasting system includes three major functional modules, namely preprocessing fishery production data, matching production data and environmental data, training RBF neural network and making predictions. Experiments have shown that this forecasting system can generate accurate and effective pelagic fishery knowledge.

A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms

Fault diagnosis for wireless sensor is very important to ensure signal acquisition precision. Radial basis function (RBF) neural network has strong classification ability. However, the selection of the connection weights, the hidden centers and the widths has an important influence on the classification performance of the RBF neural network in the learning process of RBF neural network. Thus, ant colony optimization is employ to gain the parameters of radial basis function neural network. Therefore, a novel method for fault diagnosis of wireless sensor based on RBF neural network and ant colony optimization is presented. The results of computational experiments show that ACO- RBF neural network has a great higher than RBF neural network.

Flooding causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Therefore, flood prediction is of prime importance for reducing loss of life and devastation to property. To model complex nature of hydrologic processes artificial neural network (ANN) tool is effectively being utilized for modelling different nonlinear relationships, and has proved to be an appropriate method for flood prediction. Present study investigated relative accuracy of radial basis function neural network (RBFNN) and support vector machine (SVM) models combined with Firefly Algorithm (FA) in predicting river flood discharge and contrasted with that of regular ANN, RBFNN and SVM models. Monthly river flow data of Silchar and Dholai stations located in Cachar district of Assam, India are utilized for the present study. For assessing model performance, coefficient of determination (R2), mean square error (MSE) and root mean square error (RMSE) were measured. Evaluation of outcomes shows that both RBF-FA (radial basis function - firefly algorithm) and SVM-FA (support vector machine - firefly algorithm) hybrid models give more precise forecasting results than RBFNN, FFBPNN (feed forward back propagation neural network) and SVM models. Yet, it can be observed that SVM-FA model give better forecasting outputs with R2 value 0.9818 than RBF-FA model. Results also reveal that simple SVM model performs marginally better than simple ANN model.

Radial basis function (RBF) neural network possesses the characteristics of fast training speed, reaching the global minimum and escaping from local minimum, et al. Under fewer samples, the RBF neural network model (RBFNN-based model) for credit comprehensive evaluation is established according to actual enterprise's credit state. For the 6 non-training enterprises, the comparison of the calculated results of several models shows that the accuracy of the RBFNN-based model established in this paper is the highest. It is effective and suitable to apply RBFNN to enterprise credit comprehensive evaluation

This paper uses a radial basis function (RBF) neural network for star identification. A feature vector consisting of distance and angle cosine values is constructed as a library of recognition feature stars and input to the neural network. Then, the network’s output is the number of the corresponding primary star. Comparing the training efficiency and robustness of the grid algorithm with the BP neural network and radial basis function neural network for star identification, the experimental results show that the radial basis function neural network has a strong anti-interference ability against noise and pseudo-star interference. The following conclusions are drawn from the simulation tests: the sample recognition rate of radial basis function neural network algorithm can reach 96% for star position and star addition noise; when the proportion of pseudo-star reaches 20%, the sample recognition rate of radial basis function neural network algorithm is still higher than grid algorithm and BP neural network, which indicates that the radial basis function neural network algorithm has the solid anti-interference ability to both noise and pseudo-star. Therefore, the radial basis function neural network algorithm based on star Identification has specific application prospects.

In the process of moisture content's measurement with microwave resonant cavity based on RBF (radial basis function) neural network, the relationship among resonant frequency, quality factor and environmental temperature (the inputs of network) and moisture content (the output of network) is multiple non-linear. The performance of multiple non-linear regression algorithm is the major factor that determines the accuracy of measurement. A regression algorithm based on a RBF neural network is put forward to improve the measurement result in this paper. The RBF neural network regression algorithm can effectively avoid the BP algorithm disadvantages such as getting into local minimum and the low efficiency, and has the advantages such as high generalization precision and rapid convergence. Consequently, the measurement accuracy is enhanced. In this paper, experimental results show that the high-precision measurement value of moisture content can be obtained with the multiple non-linear regression algorithm based on RBF neural network.

The application of radial basis function neural networks (RBFN) to predict the ultimate torsional strength of reinforced concrete (RC) beams is explored in this study. A database on torsional failure of RC beams with rectangular section subjected to pure torsion was retrieved from past experiments in the literature; several RBFN models are sequentially built, trained and tested. Then the ultimate torsional strength of each beam is determined from the developed RBFN models. In addition, the predictions of the RBFN models are also compared with those obtained using the ACI 318 Code equations. The study shows that the RBFN models give reasonable predictions of the ultimate torsional strength of RC beams. Moreover, the results also show that the RBFN models provide better accuracy than the existing ACI 318 equations for torsion, both in terms of root-mean-square error and coefficients of determination.

The neural network data are usually characterized by abruptness, nonlinearity and time variability, and thus, it is difficult to yield accuracy results of network traffic prediction based on a traditional radial basis function neural network that has the shortcomings of slow convergence and easily falling to local optimum. To improve the accuracy of network traffic prediction and optimize the method of parameter and structure setting for a neural network, a model of radial basis function neural network with improved particle swarm optimization algorithm is proposed by referring to the related theories of network traffic and phase space reconstruction. The improved particle swarm optimization algorithm can adjust the inertia weight and the learning factors, and make $$t$$ -distribution mutation of particles’ positions via global extremum to avoid local convergence and thereby improving its global searching capacity; with such an algorithm, the parameters of radial basis function neural network are optimized; then, in order to verify the algorithm’s effectiveness, the radial basis function neural network is trained to become an optimal prediction model, which is adopted for the prediction of two typical chaotic time series and the real network traffic. It is then compared with the traditional radial basis function neural network model and the radial basis function prediction model by improved particle swarm optimization; and the simulations result shows that the application of this model improves the accuracy of network traffic prediction, and demonstrates the algorithm’s feasibility and effectiveness for network traffic prediction.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

To improve the reservoir long-term runoff forecasting accuracy. Adaptive regulation ant colony system algorithm (ARACS) is proposed. The forecast model is set up by using an adaptive regulation ant colony system algorithm and the radial basis function (RBF) neural network combined to form ARACS-RBF hybrid algorithm. Form the reservoir long-term runoff forecast model based on the hybrid algorithm. Then carry out the reservoir long-term runoff forecast by using the method and history runoff data. The result shows the convergence of method is faster and forecast accuracy is more accurate than that of the traditional ant colony system algorithm-RBF neural network and RBF neural network. The method improves forecast accuracy and improves the RBF neural network generalization capacity; it has a high computational precision, and in 98% of confidence level the average percentage error is not more than 6%. The hybrid algorithm can be used efficaciously in long-term runoff forecasting of the reservoir and river.

Mathematics achievement could be influenced by cognitive and non-cognitive factors. The potential variable of cognitive factor is metacognition, whereas non-cognitive factors include Economic, Social, and Cultural Status (ESCS), resilience, life satisfaction, happiness, pride, fear, sadness, and gender. Those variables involve numerical and categorical data. For this reason, this study aims to apply the Radial Basis Function Neural Network (RBFNN) model with ensemble clustering to model the relation between cognitive and non-cognitive aspects and mathematics achievement. The RBFNN is a soft computing approach based on the neural network model and has been shown as an effective model and free of assumption. The ensemble clustering is a process in RBFNN modeling to capture the independent variables involving the numerical and categorical data. It employs K-means clustering for the numerical data and K-modes for categorical data and combines the results of those two methods. The data used in this study are published by PISA (Program for International Student Assessment) 2018. The results show that the RBFNN with ensemble clustering deliver good performance in modeling the students’ mathematics achievement based on the cognitive and non-cognitive factors in terms of prediction accuracy. Other than RBFNN model, the use of cognitive and non-cognitive factors involving in this study also contributes to the high accuracy prediction. This further emphasizes that these factors are good predictors of mathematic achievement. Additionally, we suggest the silhouette cluster validation in the clustering process, since it leads to the number of hidden neurons of the best RBFNN model.

In order to solve the problem that how to evaluate the complex system support concept, an evaluation method based on Radial Basis Function (RBF) neural network model was presented. Through researching the support system overall design characteristics and elements of support, on this basis, evaluation parameters of support concept were abstracted. Support concept evaluation model based on RBF was established and a mature and stable RBF neural network was trained to calculate the comprehensive evaluation value for support concept. Finally, the further demonstration and verification of the method are given through specific case application and compared with the result for evaluation results of data envelopment analysis (DEA) model.

Artificial Neural Network (ANN) simulates the structure and function of human brain. It has the abilities of parallel information processing, distributed storage and self-learning and reasoning. ANN features fault tolerance, nonlinearity, nonlocality, nonconvexity, etc., and is suitable for identifying and mapping fuzzy information or complex nonlinear relationship. Combined with the characteristics of domestic water consumption, industry water consumption and agriculture water consumption, the influencing factors are analyzed. A Radial Basis Function (RBF) Neural Network model is established for water demand prediction, using 17 water demand predication factors as input of the network. On output layer, the four nodes include urban household water demand, rural household water demand, industrial water demand and agricultural water demand. Dynamic Clustering Learning algorithm is used to determine RBF width, cluster center, number of nodes in hidden layer and weight. The number of hidden layer determined by network learning is 8. The relative error of three years are 2.74%, 3.33% and 1.41% respectively. The results show that RBF neural network has such advantages that the output is independent the initial weight value and the convergence speed is faster. And a better forecasting result is achieved through such a model.