Genetic Algorithm Radial Basis Function Research Articles

Retracted: Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA).

[This retracts the article DOI: 10.1155/2021/2000159.].

Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

One of the key concerns in the 21st century, alongside the growing population, is the increase in energy consumption and the resulting global warming. The impact of CO2, a prominent greenhouse gas, has garnered significant attention in the realm of CO2 capture and gas purification. CO2 absorption can be enhanced by introducing some additives into the aqueous solution. In this study, the accuracies of some of the most up-to-date computational approaches are investigated. The employed machine learning methods are hybrid-adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS), least-squares support vector machines (LSSVM) and genetic algorithm-radial basis function (GA-RBF). The developed models were used in estimating the solubility of CO2 in binary and ternary amines aqueous solutions. i.e. blends of monoethanolamine (MEA), triethanolamine (TEA), aminomethyl propanol (AMP), and methyldiethanolamine (MDEA). This modeling study was undertaken over relatively significant ranges of CO2 loading (mole of CO2/mole of solution) as a function of input parameters, which are 0.4–2908 kPa for pressure, 303–393.15 K for temperature, 36.22–68.89 g/mol for apparent molecular weight, and 30–55 wt % for total concentration. In this work, the validity of approaches based on different statistical graphs was investigated, and it was observed that the developed methods, especially the GA-RBF model, are highly accurate in estimating the data of interest. The obtained AARD% values for the developed models are 18.63, 8.25, 12.22, and 7.54 for Hybrid-ANFIS, PSO-ANFIS, LSSVM, and GA-RBF, respectively.

Applied machine learning-based models for predicting the geomechanical parameters using logging data

Geomechanical (GM) parameters play a significant role in geomechanical studies. The calculation of GM parameters by analyzing finite rock samples is very limited. The GM parameters show a nonlinear trend; thus, applying empirical relationships is unreliable to predict their quantities. Machine learning (ML) methods are generally used to improve the estimation of such parameters. Recent researches show that ML methods can be useful for estimating GM parameters, but it still requires analyzing different datasets, especially complex geological datasets, to emphasize the correctness of these methods. Therefore, the aim of this study is to provide a robust recombinant model of the ML methods, including genetic algorithm (GA)–multilayer perceptron (MLP) and genetic algorithm (GA)–radial basis function (RBF), to estimate GM parameters from a complex dataset. To build ML models, 48,370 data points from six wells in the complicated Norwegian Volve oil field are used to train GA–MLP and GA–RBF methods. Moreover, 20,730 independent data points from another three wells are used to verify the GM parameters. GA–MLP predicts GM parameters with the root-mean-squared error (RMSE) of 0.0032–00079 and coefficient determination (R2) of 0.996–0.999. It shows similar prediction accuracy when used to an unseen dataset. Comparing the results indicates that the GA–MLP model has better accuracy than the GA–RBF model. The results illustrate that both GA–MLP and GA–RBF methods perform better at estimating GM parameters compared to empirical relationships. Concerns about the integrity of the methods are indicated by assessing them on another three wells.

MMC-based PV-Fed STATCOM with Hybrid GA-RBFNN for PQ Enhancement

Power quality (PQ) is a consequential factor, which is highly influenced by the functioning of the transmission and distribution network. In the distribution systems, voltage swells, sags, harmonics and flickers are regarded as major power quality issues. The Static Synchronous Compensator (STATCOM) is one among the specialized power devices, which has gained considerable interest for its capacity of enhancing the performance of power systems. This paper analyses the power quality issues of a photovoltaic (PV) system adopting a STATCOM-based five-level Modular Multilevel Converter (MMC). An interleaved quadratic boost converter (QBC) is employed in this work, which escalates the PV output in a wider range. An Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed for obtaining the fast, efficient and flexible control of the DC-DC converter. The MMC plays a crucial part in mitigating power quality issues as it has the beneficial impacts such as scalability, power quality and modularity. In this approach, a five-level MMC is implemented, which transfers active power to the load by maintaining the power factor constant. The reference current generation of MMC is carried out by a hybrid Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFNN) algorithm and the pulses of MMC are generated through a hysteresis controller. It provides improved training speed and convergence along with enhanced operating efficiency of the network. The output of five-level MMC is fed to a transformer-coupled LC filter. The simulations are carried out in Matlab and a minimized Total Harmonic Distortion (THD) of 1.12% is obtained.

Rotor Transverse Slots’ Influence on Negative Sequence Loss and Heat Distribution Prediction of Large Generators Based on Improved Radial Basis Function Process Neural Network

Owing to the advantages of scientific computation and the data feature support provided by artificial intelligence technology, the theoretical exploration and application research of new computing methods for large generators, one of the most expensive energy equipment in a power system, has become a research hotspot toward solving the bearing limit and operation capacity under abnormal working conditions. Because of many factors affecting the distribution of negative sequence loss and temperature rise that have an extremely complex nonlinear relationship, the traditional calculation and prediction methods of negative sequence conditions cannot suitably reflect the time accumulation effect. Therefore, a prediction method of rotor steady-state negative sequence heating based on radial basis function process neural network is proposed in this paper, and a negative sequence working condition prediction model is established. Accordingly, this study focuses on the study of the relationship among negative sequence heating characteristics, negative sequence component proportion, and transverse slot; additionally, their influence degree, variation relationship, and main principles are further explored that provide a theoretical basis for the design and operation of large generators. As observed from the test results, the steady-state negative sequence condition prediction method based on the improved genetic algorithm radial basis function process neural network features high accuracy; it is a feasible prediction method, specifically for negative sequence conditions of large generators.

Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA).

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation

To have a sustainable economy and environment, several countries have widely inclined to the utilization of non-fossil fuels like biomass fuels to produce heat and electricity. The advantage of employing biomass for combustion is emerging as a potential renewable energy, which is regarded as a cheap fuel. Chemical constituents or elements are essential properties in biomass applications, which would be costly and labor-intensive to experimentally estimate them. One of the criteria to evaluate the energy of biomass from an economic perspective is the higher heating value (HHV). In the present work, we have applied multilayer perceptron artificial neural network (MLP-ANN), least-squares support vector machine (LSSVM), ant colony-adaptive neuro-fuzzy inference system (ACO-ANFIS), particle swarm optimization- ANFIS (PSO-ANFIS), genetic algorithm-radial basis function (GA-RBF) and new multivariate nonlinear regression (MNR) as accurate correlation methods to estimate HHVs of biomass fuels based on the ultimate analysis. 535 experimental data were gathered from literature and categorized into eight classes of by-products of fruits, agri-wastes, wood chips/tree species, grasses/leaves/fibrous materials, other waste materials, briquettes/charcoals/pellets, cereal and Industrial wastes. In the term of statistical analysis, average absolute relative deviation (AARD) authenticates that MNR and GA-RBF algorithm with %AARD of 3.5 and 3.4 could be used to estimate HHV. In addition, developed models results were compared to the results of 69 recently previously published empirical correlations and it confirms the reliability of our results. Relevency factor shows the impact of biomass elements on HHV and outlier analysis indicates the unreliable experimental data. The results of this study can be used by researchers to design and optimize biomass combustion systems.

A new evaluation and prediction model of sound quality of high-speed permanent magnet motor based on genetic algorithm-radial basis function artificial neural network.

Sound quality (SQ) has become an important index to measure the competitiveness of motor products. To better evaluate and optimize SQ, a novelty SQ evaluation and prediction model of high-speed permanent magnet motor (HSPMM) with better accuracy is presented in this research. Six psychoacoustic parameters of A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, fluctuation strength (FS), and perferred-frequency speech interference (PSIL) were adopted to objectively evaluate the SQ of HSPMM under multiple operating conditions and subjective evaluation was also conducted by the combination of semantic subdivision method and grade scoring method. The evaluation results show that the SQ is poor, which will have a certain impact on human psychology and physiology. The correlation between the objective evaluation parameters and the subjective scores is analyzed by coupling the subjective and objective evaluation results. The average error of multiple linear regression (MLR) model is 7.10%. It has good accuracy, but poor stability. In order to improve prediction accuracy, a new predicted model of radial basis function (RBF) artificial neural network was put forward based on genetic algorithm (GA) optimization. Compared with MLR, its average error rate is reduced by 3.16% and the standard deviation is reduced by 1.841. In addition, the weight of each objective parameter was analyzed. The new predicted model has a better accuracy. It can evaluate and optimize the SQ exactly. The research methods and conclusions of this paper can be extended to the evaluation, prediction, and optimization of SQ of other motors.

Software Development and Design of Network Security System Under Big Data Analysis

To explore the prediction effect of network security situational awareness on network vulnerabilities and attacks under the background of big data, this study constructs a predictive index system based on the network security situational awareness model. Based on the improved cuckoo algorithm, the cuckoo search radial basis function neural network is used to predict the situation. The weight value in the model is determined by the hierarchical analysis method, vulnerability simulation is conducted by Nessus software and network attack simulation is conducted by Snort software, and then the situation is evaluated by a fuzzy comprehensive evaluation method. Finally, Jquery and Bootstrap software is used to develop the system. The results show that the cuckoo search radial basis function model proposed in this study could predict network security situations more accurately than the radial basis function model, cuckoo search back-propagation neural network model, genetic algorithm radial basis function model and Support vector machine model based on particle swarm optimization model.

The genetic algorithm-radial basis function neural network to quickly predict aerodynamic performance of compressors

In this paper, compressor aerodynamic performance has been predicted based on throughflow theory, combined with a surrogate model, which is a combination of the Genetic Algorithm (GA) and generalized Radial Basis function (RBF) neural network. And the predicting results have been compared with those from the traditional models and spanwise mixing model, which still widely be used to predict the aerodynamic performance. We first predicted the deviation angle and total-pressure loss coefficient (TPLC) by the surrogate model, and then using these two intermediate variables connected the model with throughflow theory. The pressure ratio and efficiency, representing the compressors’ total performance parameters, are predicted and compared with experimental data. In order to increase the accuracy of prediction, a data augmentation method based on the piecewise cubic Hermite interpolation (PCHIP) algorithm is introduced to enlarge the training database. At the same time, considering the vast differences of deviation angle and loss in different working conditions as well as aerodynamic and geometric differences of rotor and stator, the database and the network should be split into six components based on the choke, the normal and the stall conditions as well as rotor and stator. Then, the performance curves of pressure ratio and efficiency can be determined by an iteration process. The predicting results are compared with experimental data, which shows that the surrogate model matches experiments much better than those from the traditional models and spanwise mixing model.

Research on Radial Basis Function Neural Network Based on Improved Hierarchical Genetic Algorithm in Early Diagnosis of Alzheimer’s Disease

With the increasing aging level of China’s population, the probability of suffering from Alzheimer’s disease also increases. Genetic Algorithm-Back-Propagation (GA-BP) were introduced and compared, and the effectiveness of Genetic Algorithm-Radial Basis Function (GA-RBF) algorithm in diagnosis and prediction of Alzheimer’s disease was verified by simulation experiments. The results showed that GA-RBF modeling time increased by about 75%, It showed that GA-RBF algorithm can be used helpful to accurately predict the cognitive status of the elderly.

LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

Modeling interfacial tension of normal alkane-supercritical CO2 systems: Application to gas injection processes

To study the gas injection scenario for successful implementation of enhanced oil recovery (EOR) processes, the prediction of interfacial tension (IFT) between injected gas and the crude oil is of paramount significance. In the present study, some intelligent methods were developed for determining IFT values between supercritical CO2 and normal alkanes. IFT was considered as a function of temperature, pressure, and molecular weight of normal alkanes. The developed methods were Multilayer perceptron (MLP), Genetic Algorithm Radial Basis Function (GA-RBF), and Conjugate Hybrid-PSO ANFIS (CHPSO-ANFIS). The average absolute percent relative errors (AAREs) for the stated techniques were found to be 2.59%, 1.39%, and 1.81%, respectively, showing that GA-RBF is the most efficient technique. This model was then compared to the other previously developed models in literature. It was also found that the current GA-RBF model with AARE of 1.39% surpasses the previously developed models. Finally, the results of the Leverage approach showed that GA-RBF model could be trusted to predict the IFT of the normal alkane- supercritical CO2 systems in the used range of pressure, temperature, and n-alkanes. This study represents the most reliable technique in predicting the IFT value between supercritical CO2 and normal alkanes to be applied in studies on gas injection processes.

Optimal control research on a manipulator’s combined feedback device by the variational method genetic algorithm radial basis function method

This article aims to improve the accuracy of each joint in a manipulator and to ensure the high-speed and real-time requirements. A method called the variational method genetic algorithm radial basis function, which is based on a combination feedback controller, is proposed to solve the optimal control problem. It is proposed a combined feedback with a linear part and a nonlinear part. We reconstruct the manipulator’s kinematics and dynamics models with a feedback control. In this model, the optimal trajectory, which was solved by the variation method, is regarded as the desired output. The other one is also established an improved genetic algorithm radial basis function neural network model. The optimal trajectory is rapidly solved by using the desired output and the improved genetic algorithm radial basis function neural network. This method can greatly improve the speed of the calculation and guarantee real-time performance while simultaneously ensuring accuracy.

Toward prediction of kinematic viscosity of biodiesel using a robust approach

ABSTRACTEspecially by using a renewable source of fuels such as biodiesel, a large number of high-quality researches have been performed on the reduction of pollution released from fossil fuels. Transesterification process is a common way for the production of biodiesel from vegetable oil, animal fat, and algae oil in the presence of alcohol and catalyst. Viscosity is one of the important physical fuel properties used in the selection of biodiesel. Experimental measurement of viscosity is a time-consuming task. Hence, in this contribution, applicability and performance of two artificial neural network-based models named least square support vector machine (LSSVM) and genetic algorithm-radial basis function (GA-RBF) for the prediction of kinematic viscosity of biodiesel were investigated. Root-mean-square error, coefficient of determination (R2), and average absolute relative deviation of each modeling were reported for each LSSVM and GA-RBF models. Modeling results show that the proposed LSSVM model is more accurate and robust than GA-RBF model.

A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant

Since the conventional thermal fluids such as water, oil, and ethylene glycol have poor thermal properties, the tiny solid particles are added to these fluids to increase their heat transfer improvement. As viscosity determines the rheological behavior of a fluid, studying the parameters affecting the viscosity is crucial. Since the experimental measurement of viscosity is expensive and time consuming, predicting this parameter is the apt method. In this work, three artificial intelligence methods containing Genetic Algorithm-Radial Basis Function Neural Networks (GA-RBF), Least Square Support Vector Machine (LS-SVM) and Gene Expression Programming (GEP) were applied to predict the viscosity of TiO2/SAE 50 nano-lubricant with Non-Newtonian power-law behavior using experimental data. The correlation factor (R2), Average Absolute Relative Deviation (AARD), Root Mean Square Error (RMSE), and Margin of Deviation were employed to investigate the accuracy of the proposed models. RMSE values of 0.58, 1.28, and 6.59 and R2 values of 0.99998, 0.99991, and 0.99777 reveal the accuracy of the proposed models for respective GA-RBF, CSA-LSSVM, and GEP methods. Among the developed models, the GA-RBF shows the best accuracy.

Application of a new version of GA-RBF neural network for simultaneous spectrophotometric determination of Zn(II), Fe(II), Co(II) and Cu(II) in real samples: An exploratory study of their complexation abilities toward MTB

The current study for the first time is devoted to the application of whole space genetic algorithm-radial basis function network (wsGA-RBFN) method to determine the content micro minerals of Zn2+, Fe2+, Co2+ and Cu2+ based on their complexes formation with methylthymol blue (MTB) spectrophotometrically in various pharmaceutical products and vegetable samples. Advantage of wsGA-RBFN compared to GA-RBFN is that centers can be located in any point of the samples spaces. Initially, the parameters controlling behavior of the system were investigated and optimum conditions were selected. Then, an exploratory analysis of complex systems was carried out by chemometrics approaches such as SVD, EFA, MCR-ALS and RAFA. The optimal parameters and conditions for constructing the proposed model of wsGA-RBFN were obtained from processing the data set of synthetic samples. Finally, wsGA-RBFN was successfully applied to the simultaneous determination of Zn2+, Fe2+, Co2+ and Cu2+ in tomato, white cabbage, red cabbage and lettuce and pharmaceutical products included iron, zinc, multi complete and B12 ampoule.

The new method of flatness pattern recognition based on GA–RBF–ARX and comparative research

Radial basis function (RBF) network combines with AutoRegressive eXogenous (ARX) model to create RBF–ARX model. RBF–ARX model can describe the global nonlinear dynamic process of the object, and its function coefficients are approximated by data-driven method. Structured nonlinear parameters optimization method is generally used to optimize model parameters, but this method is very complicated and hard to be mastered by engineers. However, genetic algorithm (GA) is relatively simple and widely used. So thought of GA optimizing RBF–ARX is generated, called GA–ARX–RBF, and applied to nonlinear dynamic flatness recognition model. Flatness pattern recognition is the basis and core of flatness control. Considering disadvantages of traditional pattern recognition method, such as limitation of recognition accuracy and poor fault tolerance, a new intelligent method of flatness pattern recognition based on the GA–RBF–ARX model is proposed. Meanwhile, in order to examine the effectiveness of RBF–ARX, another intelligent flatness recognition model based on dynamic feedback Hopfield NN is set up. Simulation proves that RBF–ARX is more suitable for flatness pattern recognition than Hopfield NN. And optimization effect by GA can achieve the desired requirements and is more precise than structured nonlinear parameters optimization method, when RBF–ARX is applied to rolling mill. GA–RBF–ARX can be a new modeling method for complex nonlinear dynamic system.

Prediction of supercritical CO2/brine relative permeability in sedimentary basins during carbon dioxide sequestration

AbstractThis study aims to accurately determine supercritical CO2/brine relative permeability, using a hybrid Genetic Algorithm‐Radial Basis Function (GA‐RBF) neural network. CO2 sequestration, along with some enhanced oil recovery (EOR) processes, demands an exact knowledge of relative permeability in order to ensure the viability of the operation. Previous studies have shown that errors in CO2/brine relative permeability data might result in a four‐fold error in injectivity estimation. This, as well as several recent studies regarding the relative permeability of CO2/brine systems, has indicated the importance of this parameter. The developed GA‐RBF model was determined to be in excellent accordance with experimental data, yielding average absolute relative deviations (AARD) of 4.66% and 2.11% for CO2 and brine relative permeability, respectively. In addition, comprehensive comparisons between classic models and the proposed GA‐RBF model have been carried out. Based on these comparisons, it may be concluded that the proposed model is superior to the classic method (simple correlation) in terms of its accuracy in determining the viability of CO2 sequestration operations. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction

The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO–GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the RBF by GA operation and the parameters of the basis and weights by a PSO–GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO–GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models; and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7–11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85billionkWh. The lowest demand is estimated for the business-as-usual scenario, and will be 88.45billionkWh.