Radial Basis Function Neural Network Structure Research Articles

Hybrid prediction method of blast furnace gas generation considering multi-operation modes in steel plants near the city

Blast furnace gas (BFG) generated in blast furnace (BF) ironmaking process can be converted into steam and electricity to provide energy for the nearby cities. Accurate prediction of BFG generation is crucial for assessing BFG surplus, which is the difficult problem faced by steel plants. A hybrid prediction method is proposed to solve it, which is named Radial Basis Function Neural Network (RBFNN) Based on Improved Genetic Algorithm (IGA) and Grey Relation Analysis (GRA). BF’s operation modes, including smooth, blown-down, blow-down to 0, blow-in, were classified to exclude the unrelated feature factors and set the adaptive prediction horizon. Then feature factors related to BFG generation are quantitatively analyzed by GRA in different operation mode, which affects the structure of RBFNN. And the key parameters of RBFNN is optimized by IGA. Proposed method is validated by a case study from a China’s steel plant. The results show the hybrid method has the obvious advantage with the MAPE of 3.31 %, 3.22 % and RMSE of 337.57 m3 in blow down, blown-in and smooth operation mode, respectively. This paper helps steel plants assess how much steam and electricity converted from BFG can be sent to its nearby cities, and contributes to energy resources utilization.

GSM-R wireless field strength coverage prediction algorithm based on PSO-RBF neural network algorithm

In the wake of the successive construction of new railway lines, new lines and existing lines are adjacent, approached and surpassed. In the early stage of line construction, if the influence of adjacent lines is not considered in subsequent planning, the original lines need to be adjusted, and the reconstruction of the base station (BS) coverage along the railway increases the hardness of buliding and the invested funds. Therefore, a Global System for Mobile Communications – Railway (GSM-R) wireless field strength coverage prediction model based on particle swarm optimization (PSO) algorithm optimized radial basis function neural network (RBFNN) was proposed. Aiming at the problem of slow network convergence caused by improper selection of network parameters and structure of RBFNN, PSO algorithm is used to optimize the parameters of RBFNN, and combined with the actual measurement data on site, a PSO-RBFNN model is established to simulate and predict the field strength coverage. The results show that the prediction effect of PSO-RBFNN is the best, followed by RBFNN, and the worst of HATA model, which is very beneficial to the future railway GSM-R wireless network coverage and provides a feasible idea.

Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network.

Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.

Signal Recognition Based on APSO-RBF Neural Network to Assist Athlete's Competitive Ability Evaluation.

The advanced analysis and research methods of big data will provide theoretical support for the integration of athletes' talent training. The advanced technological methods of big data will also give full play to the advantages of tapping the potential of talents and actively improve the success rate of grassroots young athletes. This paper proposes an improved Adaptive Particle Swarm Optimization (APSO) algorithm for the optimization of radial basis function (RBF) neural network parameters. The basic structure of RBF neural network is introduced, and the influence of parameters on the performance of RBF neural network is analyzed. The optimization method of RBF neural network parameters is analyzed, and Particle Swarm Optimization (PSO) algorithm is selected as the parameter optimization method of RBF neural network. In addition, an improved APSO algorithm is proposed according to the advantages and disadvantages of PSO and compared with other PSO algorithms. Experimental results show that the improved PSO algorithm has better accuracy. The improved PSO algorithm is applied to the parameter optimization of RBF neural network, and the experimental results prove the superiority of the proposed method. By weighting the second-level indicators, the weights of the second-level indicators of athletes' competitive ability are in order of skill, athletic quality, psychological ability, and artistic expression. Skills are the main factors that determine the level of competitive ability. Sports quality and psychological ability are important guarantees for supporting the normal performance of skills. Artistic expressiveness is a supplementary factor for competitive ability. The various elements cooperate with each other and interact with each other. The indicators do not exist alone but cooperate with each other to support the formation of the entire athletic ability system. In the content of the competitive ability index of excellent athletes, technical ability is the core, and sports quality, psychological ability, and artistic performance ability ultimately exist to serve the improvement of technical ability. The competition scores of the 100 athletes counted in this article are all above 9.0 points. The difference between APSO-RBF's action quality scores of 100 athletes and the real value is less than 3%. In terms of movement difficulty, the APSO-RBF evaluated athletes' scores are close to 1.65 points, which is basically the same as the real value.

An Evolutionary Self-organizing Cost-Sensitive Radial Basis Function Neural Network to Deal with Imbalanced Data in Medical Diagnosis

Class imbalance is a common issue in medical diagnosis. Although standard radial basis function neural network (RBF-NN) has achieved remarkably high performance on balanced data, its ability to classify imbalanced data is still limited. So far as we know, cost-sensitive learning is an advanced imbalanced data processing method. However, few studies have focused on the combination of RBF-NN and cost sensitivity. From our knowledge, only one paper has proposed a cost-sensitive RBF-NN for software defect prediction. However, the authors implemented a fixed RBF-NN structure. In this paper, a novel cost-sensitive RBF-NN that optimizes structure and parameters simultaneously is proposed to handle medical imbalanced data. Genetic algorithm (GA) and improved particle swarm optimization (IPSO) are used to optimize the structure and parameters of cost-sensitive RBF-NN respectively, and the optimization of cost-sensitive RBF-NN based on dynamic structure is realized. A cost-sensitive function determined adaptively by the sample distribution as the objective function of RBF-NN, so that it can adapt to datasets with different sample distributions. Experimental results show that the proposed cost-sensitive RBF-NN outperforms other state-of-the-art representative algorithms for five imbalanced medical diagnostic datasets in term of accuracy and area under curve (AUC). It can improve the accuracy of medical diagnosis and reduce the error rate of medical decisions.

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter referred to as “road speed”) is a key parameter for forecasting road congestion. This paper proposes a hybrid radial basis function (RBF) neural network algorithm for forecasting road speed. First, it proposes a fuzzy RBF neural network structure by combining the fuzzy logic system with the RBF neural network. Then, it incorporates factors such as weather, holidays and road grades into the input layer. Considering the uncertainty and sensitivity of the initial centre of the traditional membership function layer, it uses fuzzy C-means clustering to determine the centre and other parameters of the membership function layer. Then using the gradient descent method, it trains the weights between the fuzzy inference layer and the output layer. Finally, this paper trains the proposed hybrid RBF neural network with the traffic road network data and weather data of a city, and uses the trained hybrid neural network to predict the road speed and the congestion status. The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting accuracy, with the mean absolute percentage error (MAPE) being reduced to 6.4%. Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted values to help solve urban congestion problems.

Forecasting of Vannamei Shrimp Production Based on Weather Factors Using Radial Basis Function Neural Network Approach (Case Study: Lamongan District)

One of the economic activity sectors that can be influenced by uncertain weather conditions is aquaculture production. In Lamongan, aquaculture production especially vannamei shrimp is very dependent on the ideal weather conditions. Uncertainty of the weather conditions can cause irregular harvesting to the production of vannamei shrimp. These trend changes can have an impact on the activities of production supply chain, namely the fulfillment of the entry quota of vannamei shrimp production from agents or distributors to exporters of vannamei shrimp to meet market demand. This marketing result can increase the original income of Lamongan area. To find out the development of the trend required a forecasting process and appropriate classification based on past data using artificial neural networks. One structure of artificial neural networks that can be predicting and classifying is a radial basis function neural network (RBFNN). The structure of RBFNN is trained using K-means clustering and gradient descent method. We use average temperature, average humidity and rainfall each month starting from January 2013 until December 2017 as the actual datasets. From those datasets, the training datasets start from January 2013 until December 2016 and the remaining datasets are used as the testing datasets. Built in the Python program, the test results show that our forecasting and classification had an accuracy level with mean absolute percentage error (MAPE) 16.7%.

Neural adaptive global stability control for robot manipulators with time‐varying output constraints

SummaryIn this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods.

Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate

The neural network has the advantages of self‐learning, self‐adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS‐RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi‐information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS‐RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self‐organizing RBF neural networks (self‐organizing RBF (SORBF) and rough RBF neural networks (RS‐RBF)), VS‐RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS‐RBF.

RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network

Metamorphic testing has been successfully used in many different fields to solve the test oracle problem. However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task. Recently some machine learning approaches have been proposed to predict metamorphic relations. These approaches predicting single label metamorphic relation can alleviate this problem to some extent. However, many applications involve multi-group metamorphic relations, and these approaches are clearly inefficient. To address this problem, in this paper we propose a M ulti- L abel M etamorphic R elations prediction approach based on an improved radial basis function (RBF) neural network named RBF-MLMR. First, RBF-MLMR uses state-of-the-art soot analysis tool to generate control flow graph and corresponds labels from the source codes of programs. Second, the extracted nodes and the path properties constitute multi-label data sets for the control flow graph. Finally, a multi-label RBF neural network prediction model is established to predict whether the program satisfies multiple metamorphic relations. In order to improve the prediction results, affinity propagation and $k$ -means clustering algorithms are used to optimize the RBF neural network structure of RBF-MLMR. A set of dedicated experiments based on public programs is conducted to validate RBF-MLMR. The experimental results show that RBF-MLMR can achieve accuracy of around 80% for predicting two and three metamorphic relations.

Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various RBFNNs.

A New Optimized GA-RBF Neural Network Algorithm

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.

State of Charge Estimation for LFP Battery Using the Hybrid Method

A method to accurately estimate the state of charge (SOC) for LiFePO4(LFP) batteries is urgently required, to address the issues associated with the increased use of LPF batteries for portable devices. This paper proposes a hybrid method that combines a radial basis function (RBF) neural network and enhanced particle swarm optimization (EPSO) algorithm for SOC estimating. With a RBF neural network structure, the EPSO algorithm is used to tune the parameters of the RBF neural network, including the centers and widths of the RBF and the connection weights. The trained RBF neural network is then used to estimate the SOC of a LFP battery. In order to demonstrate the effectiveness of the proposed estimation method, the method is tested using 12.6V, 52Ah LFP batteries under varied discharging condition. The effectiveness of the proposed method is compared with the Coulomb integration method and the back propagation (BP) neural network. The results show that the proposed method outperforms the other methods.

Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA

A method to accurately estimate the State-of-Charge (SOC) for LiFePO4 (LFB) batteries is urgently required, to address the issues associated with the increased use of LFP batteries for portable devices. This paper proposes a hybrid method that combines a radial basis function (RBF) neural network, an orthogonal least-squares (OLS) algorithm and an adaptive genetic algorithm (AGA) to estimate the SOC in discharging condition. The OLS algorithm determines the optimal number of nodes in the hidden layer of the RBF neural network. With an optimal RBF neural network structure, the AGA is then used to tune the parameters of the RBF neural network, including the centers and widths of RBF and the connection weights. The trained RBF neural network is then used to estimate the SOC of a LFP battery. In order to demonstrate the effectiveness of the proposed estimation method, the method is tested using LFP batteries under several different discharging conditions. The effectiveness of the proposed method is compared with the Coulomb integration method and a back propagation (BP) neural network. The results show that the proposed method outperforms the other methods.

An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting

An accurate forecasting method for power generation of the wind energy conversion system (WECS) is urgently needed under the relevant issues associated with the high penetration of wind power in the electricity system. This paper proposes a hybrid method that combines orthogonal least squares (OLS) algorithm and genetic algorithm (GA) to construct the radial basis function (RBF) neural network for short-term wind power forecasting. The RBF neural network is composed of three-layer structures, which contain the input, hidden, and output layers. The OLS algorithm is used to determine the optimal number of nodes in a hidden layer of RBF neural network. With an appropriate RBF neural network structure, the GA is then used to tune the parameters in the network, including the centers and widths of RBF and the connection weights in second stage. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a WECS installed in Taichung coast of Taiwan. Comparisons of forecasting performance are made to the persistence method and back propagation neural network. The good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

Research on Seamless INS/GPS Integrated Navigation Algorithm

This paper proposed and discussed an INS/GPS integrated navigation method based on radial basis function neural network (RBFNN) to fuse INS and GPS data. When GPS signals were available, an adaptive Kalman filter was used to improve the estimation accuracy of INS errors, and then the RBFNN structure was trained to mimic the dynamical error model of INS. If GPS signals were unavailable, the trained RBFNN structure was utilized to bridge the GPS outages to achieve seamless navigation. Simulations in INS/GPS integrated navigation system showed the proposed method can reduce the positioning error during GPS outages.

Stochastic logic computation based RBFNN with adaptive hidden layer structure

PurposeThe purpose of this paper is to implement the hardware structure for radial basis function (RBF) neural network based on stochastic logic computation.Design/methodology/approachThe hardware implementation of artificial neural networks (ANNs) has a complicated structure and is normally space consuming due to huge size of digital multiplication, addition/subtraction, non‐linear activation function, etc. Also the unavailability of ANN hardware at an attractive price limits its use for real time applications. In stochastic logic theory, the real numbers are converted to random streams of bits instead of a binary number. The performance of the proposed structure is analyzed using very high speed integrated circuit hardware description language.FindingsStochastic theory‐based arithmetic and logic approach provides a way to carry out complex computation with very simple hardware and very flexible design of the system. The Gaussian RBF for hidden layer neuron is employed using stochastic counter that reduces the hardware resources significantly. The number of hidden layer neurons in RBF neural network structure is adaptively varied to make it an intelligent system.Originality/valueThe paper outlines the stochastic neural computation on digital hardware for implementing radial basis neural network. The structure has considered the optimized usage of hardware resources.

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

This paper presents a method for electrocardiogram (ECG) beat classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For classification stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the classification performance.

Dual-population based coevolutionary algorithm for designing RBFNN with feature selection

There are irrelevant features that are redundant or significantly degrade the learning accuracy in the real-world complex classification tasks. This paper presents a new hybrid learning algorithm based on a cooperative coevolutionary algorithm (Co-CEA) with dual populations for designing the radial basis function neural network (RBFNN) models with an explicit feature selection. This approach attempts to complete both the RBFNN construction and the feature selection simultaneously. The proposed algorithm utilizes the Co-CEA’s divide-and-cooperative mechanism, which utilizes the evolutionary algorithms executing in parallel to coevolve subpopulations, corresponding to the hidden layer structure and the dominate features respectively. The algorithm adopts the binary encoding to represent the feature subset and the matrix-form mixed encoding to represent the RBFNN hidden layer structure, and a complete solution is formed via collaborations among the two subpopulations. Experimental results illustrate that the proposed algorithm outperforms other algorithms in references in terms of the classification accuracy, and it is able to obtain both prominent features and good RBFNN structure with higher prediction capability.

Fault diagnosis of nuclear power plant based on genetic-RBF neural network

This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimise the possible nuclear disasters caused by inaccurate diagnoses done by operators. Combined binary and decimal coding methods are employed in this work based on Radial Basis Function Neural Network (RBFNN) structure. This underlying RBFNN structure is further trained through genetic optimisation algorithm based on known frequent failure conditions from a nuclear power plant's condensation and feed-water system. It is found that the proposed Genetic-RBFNN (GRBFNN) method not only makes the original neural network smaller in terms of computation and realisation but also improves the diagnosis speed and accuracy.