Genetic Algorithm-based Back Propagation Neural Network Research Articles

Adaptive STCPF for UCA pose estimation with improved GA-BPNN and multiple fading factors

In response to the challenge of pose estimation accuracy for unmanned carrier-based aircraft in complex maritime landing environments, we propose an adaptive strong tracking cubature particle filtering method based on an improved Genetic Algorithm-Based Backpropagation Neural Network (GA-BPNN) and multiple fading factors. Despite the effectiveness of strong tracking cubature Kalman filtering (STCKF) in adjusting the covariance of prediction estimation errors using fading factors, it still has limitations in terms of model correction and parameter estimation. Addressing this issue, our research delves deeper into the role of fading factors in adjusting process noise covariance and introduces two methods to maintain covariance matrix symmetry: direct matrix solving and trace matrix solving. This novel approach directly implements real-time dynamic feedback correction of system model parameters within the filtering framework, effectively resolving filtering divergence issues while maintaining a high level of conceptual clarity and interpretability. To further enhance the algorithm’s performance, we introduce an improved neural network algorithm to compensate for linear approximation errors in fading factor calculations, thereby improving filtering accuracy. Moreover, considering the limitations of cubature Kalman filtering when dealing with non-Gaussian systems, we integrate the adaptive STCKF based on the improved GA-BPNN into particle filtering as an importance function, aiming to enhance non-Gaussian processing performance and alleviate particle degeneracy issues. In conclusion, both simulation and real-world data tests confirm the superiority of our research approach.

Fabrication and Characterization of Lead-Free BNT-6BT Ultrasonic Transducers Designed by an Intelligent Optimization Algorithm

Lead-free piezoelectric material-based ultrasonic transducers have been researched for several years, but the inefficient properties and design difficulties have troubled lead-free ultrasonic transducers for a long time. To improve the performance and design efficiency of lead-free ultrasonic transducers, in this work, an equivalent circuit model and intelligent optimization algorithm were combined for use in a transducer design. Firstly, 0.94(Bi0.5Na0.5)TiO3-0.06BaTiO3(BNT-6BT) lead-free piezoelectric ceramics were prepared and characterized. Then, BNT-6BT ceramics were used to fabricate the ultrasonic transducers. An equivalent circuit model-based software, PiezoCAD, and a genetic algorithm-based back-propagation neural network were used to optimize the design of the transducers. A 3.03 MHz center frequency and 60.3% −6 dB bandwidth of the optimized transducers were achieved, which were consistent with the neural networks optimization results. To verify the application potential of the lead-free transducers, tungsten rods phantom imaging and polystyrene spheres with 300 μm diameter manipulation were completed by the transducers, and the experiment results indicate that the BNT-6BT lead-free transducers have great potential in further biological and biomedical applications.

Stability Analysis of Geotechnical Landslide Based on GA-BP Neural Network Model.

Rock and soil landslides, a regular geological disaster in engineering construction, endanger national property and, in severe circumstances, result in a huge number of casualties. A set of methods for landslide stability analysis and prediction has been established, with the academic idea of “geological process mechanism analysis-quantitative evaluation” at its core, combined with detailed field investigation of geological hazards, forming a relatively complete technical route for research on landslide stability analysis. The work of this paper can be summarized as follows: (1) Introduce the research status of geotechnical landslide stability at home and abroad and the current development trend of neural network. (2) Through the collected sample database, take the training function and the number of hidden layer neurons as variables to optimize the BP neural network, and combine the optimized BP neural network with the genetic algorithm to construct the GA-BP neural network. (3) The stability coefficients of the BP neural network, the genetic algorithm based back propagation neural network (GA-BPNN), and the limit equilibrium technique are analyzed and compared. The findings imply that landslide stability can be assessed using neural networks. GA-BPNN is a viable alternative to back propagation neural network (BPNN). The algorithm is more accurate, has a faster convergence rate, and is more stable.

Compressive strength of rubberized concrete: Regression and GA-BPNN approaches using ultrasonic pulse velocity

Rubberized concrete is one of the solutions to the disposal problem caused by large amounts of untreated waste rubber. To assess the performance of existing concrete structures, non-destructive-testing techniques offer a direct, quick, safe and reliable means of assessing the performance of concrete structures. Several researchers have proposed relationships, often of an exponential form, between the Ultrasonic Pulse Velocity (UPV) and compressive strength of rubberized concrete. This paper aims to propose a regression model and a genetic algorithm based backpropagation neural network (GA-BPNN) model that can be used to estimate compressive strength of rubberized concrete with UPV for different applications and accuracy requirements. Regression model in comparisons with GA-BPNN, firstly requires less computation work and will be easier for site measurement or environments without computers or certain softwares, secondly it has no barriers for users who are not familiar with machine learning models to approximately estimate the strength. The regression model comprises an Adjusted Regression Model which is a multi-variable non-linear model adjusted based on the ordinary exponential model incorporating other principal parameters, hence representing an improvement on the existing exponential model and two types of Stepwise Regression Model (pure linear and pure quadratic) will be employed. To achieve this, a database containing 158 pairs of data collected from previous literature is compiled. Results indicate that both three types of regression models and GA-BPNN are capable of effectively predicting the compressive strength of rubberized concrete with reasonable values of statistical indexes. More specifically, among three types of regression model, the pure quadratic stepwise regression model has relatively better performance with higher R and lower root-mean-square error values. Results also support that GA-BPNN has the highest accuracy compared to regression models and is proven to be reasonable for more precise estimations.

Prediction of submarine pipeline equilibrium scour depth based on machine learning applications considering the flow incident angle

Scouring below submarine pipelines, due to fluid, structure and soil material interaction, is a complex phenomenon involving numerous effective parameters. In this research, machine learning methods, such as the GA-BP (Genetic Algorithm based Back Propagation) neural network, RBF (Radial Basis Function) and SVM (Support Vector Machine) are presented and prediction models are built for forecasting pipeline equilibrium scour depth. The results of the prediction models are compared with observed data, which shows that the GA-BP model provides the best predictive performance for scour depth exhibiting highest correlation coefficient and lowest Root Mean Square Error as compared with RBF, SVM in live-bed conditions. Results of the sensitivity analysis indicate that Froude number (Fr) is the most effective parameter for predicting the scouring depth below the pipeline. With the increase of flow incident angle, its influence on the predicted scour depth results becomes more obvious.

Location of abnormal energy consumption and optimization of energy efficiency of hydraulic press considering uncertainty

As a high energy consumption machine, there is plenty of abnormal energy consumption in the operation of a hydraulic press, which leads to energy loss and reduces energy efficiency. The time series of energy consumption data are characterized by intense uncertainty, nonlinearity, and non–Gaussianity. The traditional method is used to locate it, which has the problems of a large error and uncertain locating result. Therefore, an abnormal energy consumption locating technique considering uncertainty is developed. To quantitatively evaluate the correlation between abnormal machine components and energy consumption, an uncertainty evaluation method combining entropy weight and fuzzy theory is proposed, which aims to provide a decision basis for the location of abnormal energy consumption. To accurately locate abnormal energy consumption, a novel localization technique is proposed, which is called genetic algorithm–based wavelet neural network. Firstly, the energy consumption data is processed by wavelet packet decomposition and reconstruction. The energy value is calculated, and then the dimension reduction is performed by principal component analysis. Finally, energy value is input into the genetic algorithm–based backpropagation neural network model for training and anomaly location. We integrate the proposed method into the energy management system and perform energy efficiency optimization analysis. The results show that the proposed method can improve the energy efficiency of a hydraulic press, reduce energy cost, and reduce environmental pollution. This provides a solution for solving the cleaner production problem of high energy consumption manufacturing industry and realizing sustainable intelligent manufacturing.

Complementary Ensemble Adaptive Local Iterative Filtering and Its Application to Rolling Bearing Fault Diagnosis

A fault diagnosis model for rolling bearing based on complementary ensemble adaptive local iterative filtering (CEALIF), Laplacian score (LS) feature selection, and genetic algorithm-based backpropagation neural network (GA-BPNN) is proposed in this article. When the rolling bearing fails, the field-measured waveforms usually shows strong nonlinearity and non-stationary characteristics. Adaptive Local Iterative Filtering (ALIF) is an alternative novel approach to empirical mode decomposition to decompose complex signals into multiple intrinsic mode functions (IMFs), but modal aliasing will occur in actual processing. Aiming at the phenomenon of modal aliasing, a noise-assisted analysis methodology, namely complementary ensemble adaptive local iterative filtering, which could overcome the modal aliasing problem of ALIF. This article applies it to the pre-processing of rolling bearing time series. Then, the time domain (TD) statistical features of the IMFs, their Fourier frequency domain (FD) features, and the time frequency domain (TFD) energy features are extracted to capture the fault information. Meanwhile, to avoid feature redundancy and enhance the diagnostic performance, the LS is adopted to rank the features to improve the fault characteristics. Subsequently, the optimized feature vectors are entered into the GA-BPNN to automatically achieve the fault type recognition. The experimental data analysis results of rolling bearings indicate that the model can effectively diagnose the degree and type of failure.

Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R2 = 0.38 and RMSE = 87.97) and SVR (R2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

Monitoring maize growth on the North China Plain using a hybrid genetic algorithm-based back-propagation neural network model

Crop growth and early yield information is crucial for the establishment and adjustment of agricultural management plans. Timely, precise and regional assessments of crop growth conditions and production greatly benefit the national economy and agriculture. In this study, remotely sensed leaf area index (LAI) and vegetation temperature condition index (VTCI) data retrieved from the Global LAnd Surface Satellite (GLASS) and Moderate-resolution Imaging Spectroradiometer (MODIS) data were selected as the key indicators of maize growth and a hybrid genetic algorithm (GA)-based back-propagation neural network (BPNN) (GA-BPNN) model was applied to provide complementary information on maize growth at the main growth stage. GA-BPNN models with different architectures were established, and an architecture with 10, 9 and 1 nodes in the input, hidden and output layers, respectively, achieved the best training and testing performance. The root mean square error (RMSE) values of the training and testing samples were 588.2 kg/ha and 663.4 kg/ha, respectively. Thus, the hybrid model with the best architecture (10-9-1) was selected to calculate the values of the integrated growth monitoring index (G) at the regional scale with a 1 km spatial resolution in the study area from 2010 to 2018. The results showed that the monitored maize growth well reflected the actual situation and the correlations between G values and sites’ measured variables, such as the maize yield and soil relative humidity, were higher than that of a pure BPNN model. The linear relationship between the GA-BPNN-based G values and maize yields was analyzed to estimate the maize yield in the North China Plain. Most of the RMSE and mean absolute percentage error (MAPE) values between the estimated and actual maize yields were less than 700.0 kg/ha and 10.0%, respectively. Considering that the estimation errors of most statistical samples were small and there were no obvious differences between the estimated maize yields in the adjacent regions of the northern, central and southern plain, the GA-BPNN-based yield estimation model provided reliable and spatially continuous estimates of maize yield.

The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability

This study systemically investigated the application of core/shell technique to improve powder compactability. A 28-run Design-of-Experiment (DoE) was conducted to evaluate the effects of the type of core and shell materials and their concentrations on tensile strength and brittleness index. Six machine learning algorithms were used to model the relationships of product profile outputs and raw material attribute inputs: response surface methodology (RSM), Support Vector Machine (SVM), and four different types of artificial neural networks (ANN), namely, Backpropagation Neural Network (BPNN), Genetic Algorithm Based BPNN (GA-BPNN), Mind Evolutionary Algorithm Based BPNN (MEA-BPNN), and Extreme Learning Machine (ELM). Their predictive and generalization performance were compared with the training dataset as well as an external dataset. The results indicated that the core/shell technique significantly improved powder compactability over the physical mixture. All machine learning algorithms being evaluated provided acceptable predictability and capability of generalization; furthermore, the ANN algorithms were shown to be more capable of handling convoluted and non-linear patterns of dataset (i.e. the DoE dataset in this study). Using these models, the relationship of product profile outputs and raw material attribute inputs were disclosed and visualized.

A Genetic Algorithm-based BP Neural Network Method for Operational Performance Assessment of ATC Sector

To assess operational performance of air traffic control sector, a multivariate detection index system consisting of 5 variables and 17 indicators is presented, which includes operational trafficability, operational complexity, operational safety, operational efficiency, and air traffic controller workload. An improved comprehensive evaluation method, is designed for the assessment by optimizing initial weights and thresholds of back propagation (BP) neural network using genetic algorithm. By empirical study conducted in one air traffic control sector, 400 sets of sample data are selected and divided into 350 sets for network training and 50 sets for network testing, and the architecture of genetic algorithm-based back propagation (GABP) neural network is established as a three-layer network with 17 nodes in input layer, 5 nodes in hidden layers, and 1 node in output layer. Further testing with both GABP and traditional BP neural network reveals that GABP neural network performs betterthan BP neural work in terms of mean error, mean square error and error probability, indicating that GABP neural network can assess operational performance of air traffic control sector with high accuracy and stable generalization ability. The multivariate detection index system and GABP neural network method in this paper can provide comprehensive, accurate, reliable and practical operational performance assessment of air traffic control sector, which enable the frontline of air traffic service provider to detect and evaluate operational performance of air traffic control sector in real time, and trigger an alarm when necessary.

Breakout prediction for continuous casting using genetic algorithm-based back propagation neural network model

In this paper, the effectiveness of the genetic algorithm-based back propagation (GABP) neural network model and its application to the breakout prediction in the continuous casting process are investigated. The formation of the sticking-type breakouts and the prediction principle of thermocouple thermometry method are analysed firstly. Then the genetic algorithm-based back propagation neural network model is proposed by fusing genetic algorithm (GA), and error back propagation neural network to offset the demerits of one paradigm by the merits of another. Finally, the GABP neural network model is applied to the breakout prediction in the continuous casting process; and the feasibility of the model is verified by the testing result with the accuracy rate of 97.56% and the prediction rate of 100%.

Thermal error optimization modeling and real-time compensation on a CNC turning center

Thermal errors are the largest contributor to the dimensional errors of a workpiece in precision machining. The error compensation technique is an effective way of reducing thermal errors. Accurate modeling of errors is a key part of error compensation. The thermal errors of a machine tool can be treated as the superposition of a series of thermal error modes. In this paper, five key temperature points of a turning center were obtained based on the thermal error mode analysis. A thermal error model based on the five key temperature points was proposed by using genetic algorithm-based back propagation neural network (GA-BPN). The GA-BPN method improves the accuracy and reduces computational cost for the prediction of thermal deformation in the turning center. A thermal error real-time compensation system was developed based on the proposed model. An experiment was carried out to verify the performance of the compensation system. The experimental results show that the diameter error of the workpiece reduced from about 27–10 μm after implementation of the compensation.