Prediction of heart abnormalities using Particle Swarm Optimization in Radial Basis Function Neural network

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.

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

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.

The increasing use of computer numerical control (CNC) machines requires better prediction of the reliability of their servo control systems. A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. It can overcome the disadvantages of conventional methods, which are time consuming and resource intensive. The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. Two common problem of the PSO: local minimization and slow convergence were solved by the IPSO. “Zero failure” data preprocessing, data normalization, and small-sample data enhancement were performed on the original data. A homogenized sampling method is proposed to extract training and testing samples. Experimental results show that the improved PSO-based RBF neural network is superior to back propagation (BP) and RBF networks in terms of accuracy in servo system reliability prediction.

Improving multiclass pattern recognition with a co-evolutionary RBFNN

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.

Digital image recognition of plant diseases could reduce the dependence of agricultural production on the professional and technical personnel in plant protection field and is conducive to the development of plant protection informatization. In order to find out a method to realize image recognition of plant diseases, four kinds of neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used to distinguish wheat stripe rust from wheat leaf rust and to distinguish grape downy mildew from grape powdery mildew based on color features, shape features and texture features extracted from the disease images. The results showed that identification and diagnosis of the plant diseases could be effectively achieved using BP networks, RBF neural networks, GRNNs and PNNs based on image processing. For the two kinds of wheat diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 97.50% with the fitting accuracy equal to 100% while RBF neural networks were used. For the two kinds of grape diseases, the best prediction accuracy was 100% with the fitting accuracy equal to 100% while BP networks, GRNNs or PNNs were used, and the best prediction accuracy was 94.29% with the fitting accuracy equal to 100% while RBF neural networks were used.

An offline optimization approach based on energy storage management response in a microgrid was not fast and not reliable enough to control and adjust the system efficiently after the loss of the utility grid. Thus, it causes system inefficiency and collapse in the presence of violent changes of loads or outage of distributed generations. To solve such a problem, more real-time management is needed. Any changes in loads/generations should be compensated successfully by a battery energy storage system (BESS) in a short period of time. This paper presents a new method for the intelligent online management of both active and reactive power of a BESS based on a radial basis function neural network (RBFNN) incorporating particle swarm optimization (PSO) to prevent the stand-alone microgrid from instability and system collapse. BESS is centrally controlled by a controller developed by the proposed RBFNN. PSO is used to determine the optimized active and reactive power at every load/generation changing situation to monitor the effect of system frequency, voltage, and reference power regulation. These optimized power data are then employed as target data for the RBFNN generalization and training process. To enable the online updating of the operating parameters, the proposed RBFNN is implemented in the management process. With an appropriate RBFNN training, the optimum active and reactive power can directly be obtained without the necessity of performing the PSO optimization process at any change of load/generation. The results show that the predictive results of the proposed RBFNN model only slightly differed from the target results based on PSO and have a minimum statistical error compared to the predictive results based on the multilayer perceptron neural network (MLPNN) model. The proposed RBFNN is suitable for the online estimation of the active and reactive power of BESS and can be used for real-time energy storage management as an online controller.

ECG beat classification using particle swarm optimization and radial basis function neural network

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.

A novel hybrid particle swarm optimization (HPSO) is proposed, which the gradient descent learning algorithm is combined with modified particle swarm optimization (MPSO). Firstly, the MPSO was determined by linearly decreasing inertia weight and constriction factor weight to speed up global search, also crossover and mutation operation was embedded to avoid the common defect of premature convergence. Furthermore, gradient descent learning algorithm searched for the model parameter of radical basis function neural networks (RBFNN) to speed up the local search. Using the proposed HPSO algorithm based on RBFNN, we simulated the chaotic time series prediction of Henon map to test the validity. Simulation results show that the HPSO can accurately predict chaotic time series. lt provides an attractive approach to study the properties of complex nonlinear dynamic system

This paper proposes a prediction model for forecasting the hard landing problem. The landing phase has been demonstrated the most dangerous phase in flight cycle for fatal accidents. The landing safety problem has become one of the hot research problems in engineering management field. The study concentrates more on the prediction and advanced warning of hard landing. Firstly, flight data is preprocessed with data slicing method based on flight height and dimension reduction. Subsequently, the radial basis function (RBF) neural network model is established to predict the hard landing. Then, the structure parameters of the model are determined by the K-means clustering algorithm. In the end, compared with Support Vector Machine and BP neural network, the RBF neural network based on K-means clustering algorithm model is adopted and the prediction accuracy of hard landing is better than traditional ways.

The radial basis function neural network model is optimized for application scenarios such as uneven data distribution, privacy sensitivity, or lack of direct access to the original data. Firstly, federated learning is applied to the radial basis function neural network, facilitating cross-multicenter distributed collaborative training. Then, genetic algorithms are used to optimize the hyperparameters of the federated radial basis function neural network model. Comparative experiments were conducted using the publicly available National health and nutrition examination survey 2013–2014 (NHANES) age prediction subset dataset and the proprietary HEART dataset. Compared with traditional centralized learning methods, the proposed model demonstrates superior performance, providing new ideas and methods for solving distributed learning and data privacy problems.

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.