Research on Federated Radial Basis Function Neural Network Based on Genetic Algorithm

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

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.

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.

(1) To present hearing threshold data from a recent nationally representative survey in the United States (National Health and Nutrition Examination Survey, 1999-2004) in a distributional format that might be appropriate to replace Annex B in international (ISO-1999) and national (ANSI S3.44) standards and (2) to compare these recent data with older survey data (National Health Examination Survey I, 1959-1962) on which the current Annex B is based. Better-ear threshold distributions (selected percentiles and their confidence intervals) were estimated using linear interpolation. The 95% confidence intervals for the medians for the two surveys were compared graphically for each of the four age groups and for both men and women. In addition, we calculated odds ratios comparing the prevalences of better-ear hearing impairment (thresholds > 25 dB HL) between the two surveys, for 500, 1000, 2000, and 4000 Hz, and for their four-frequency average. Across age and sex groups, median thresholds were lower (better) in the 1999-2004 survey at 500, 3000, 4000, and 6000 Hz (8000 Hz was not tested in the 1959-1962 survey). For both men and women, the prevalence of hearing impairment was significantly lower in 1999-2004 at 500, 2000, and 4000 Hz, but not at 1000 Hz. For men and women of a specific age, high-frequency hearing thresholds were lower (better) in 1999-2004 than in 1959-1962. The prevalences of hearing impairment were also lower in the recent survey. Differences seen at 500 Hz may be attributable at least in part to changes in standards for ambient noise in audiometry. The National Health and Nutrition Examination Survey 1999-2004 distributions are offered as a possible replacement for Annex B in ISO-1999 and ANSI S3.44.

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.

Epidemiology of latex sensitization

In this work, the right training criterion for optimal prediction of the desired output by the neural network model was our focus. The application of the early stopping technique in the neural network training of radial basis function model in comparison to the same model neural network training without application of early stopping technique was the basis for the work and the work validates the importance of early stopping technique for good network generalization through prevention of overfitting. An over-fitted radial basis function neural network model possesses the tendency for dataset memorization including noise and thus, poorly learns the dataset pattern. From the training results, the application of an early stopping technique inclusive of other vital hyper-parameters during the training of the radial basis function model demonstrates a measure that is very imperative in the prevention of over-fitting during network training. The training results show that the radial basis function model training without the application of an early stopping technique displayed a “learning and slow” phenomenon where the model stopped learning the dataset at a stage without optimum prediction of the dataset pattern. This phenomenon was evaded by the application of an early stopping technique at the ratio of 70%:15%:15% for training, testing, and validation of the standardized dataset. The radial basis function model neural network training regression results show optimum prediction results of R=0.99420 with a training set of R=0.9985 and a test set of R=0.9899 showing very close prediction of the training set by the test set on training with early stopping technique in comparison to the model training without the application of early stopping technique where R= 0.97200 with a training set of R= 0.9840 and a test set of R=0.9612 showing very wide prediction of the training set by the test set. Overfitting often results in good model training but poor test error as the network model learns the wrong structure in the training data or stops learning at a point as seen from the work. This was optimally evaded on the application of the early stopping technique in the work for robust prediction of signal power loss using a long-term evolution dataset.

A comparison of empirically based steady-state models for vapor-compression liquid chillers

This paper proposes an optimized prediction algorithm of radial basis function neural network based on an improved artificial bee colony (ABC) algorithm in the big data environment. The algorithm first uses crossover and mutation operators of the differential evolution algorithm to replace the search strategy of employed bees in the ABC algorithm, then improves the search strategy of onlookers in the ABC algorithm to produce an optimal candidate food source near the population. The algorithm can better balance local and global searching capability. To verify the efficiency of this algorithm in the big data environment, apply it to Lozi and Tent chaotic time series and measured traffic flow time series, and then compare it with K-nearest neighbor model, radial basis function (RBF) neural network model, improved back propagation neural network model, and RBF neural network based on a cloud genetic algorithm model. The experimental results indicate that the accuracy of prediction for Lozi and Tent chaotic time series and the measured traffic flow improves greatly in the big data environment using the proposed algorithm, which proves the effectiveness of the proposed algorithm in predicting traffic flow time series.

Calibration of Cystatin C in the National Health and Nutrition Examination Surveys (NHANES)

Via Granular Computing (GrC), one can create effective computational frameworks for obtaining information from data, motivated by the human perception of combining similar objects. Combining knowledge gained via GrC with a Fuzzy inference engine (Neural‐Fuzzy) enable us to develop a transparent system. While weighting variables based on their importance during the iterative data granulation process has been proposed before (W‐GrC), there is no work in the literature to demonstrate effectiveness and impact on Type‐2 Fuzzy Logic systems (T2‐FLS). The main contribution of this paper is to extend W‐GrC, for the first time, to both Type‐1 and Type‐2 models known as Radial Basis Function Neural Network (RBFNN) and General Type‐2 Radial Basis Function Neural Network (GT2‐RBFNN). The proposed framework is validated using popular datasets: Iris, Wine, Breast Cancer, Heart and Cardiotocography. Results show that with the appropriate selection of feature weight parameter, the new computational framework achieves better classification accuracy outcomes. In addition, we also introduce in this research work an investigation on the modelling structure's interpretability (via Nauck's index) where it is shown that a good balance of interpretability and accuracy can be maintained.

The technology for water dispatch is very complex in South-to-North Water Diversion Middle Route Project, and it is necessary to take advantage of automation system for water delivery. The model for calculating flow rate is important to water dispatch, but traditional method often needs to rectify parameters manually. A model based on radial basis function neural network is established to describe the relationship between water level, gate opening and flux. The model uses the network to simulate the optimal function between water level, gate opening and flux coefficient, and calculates the flow rate by the coefficient. By taking the new method into South-to-North Water Diversion Middle Route Project and comparing the neural network model with traditional methods, the results show that the radial basis function neural network model has higher accuracy and efficiency

This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results.

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.