BP ANN Research Articles

Ateş Böceği Algoritması Destekli Aşırı Öğrenme Makinesi ile Göğüs Kanseri Veri Kümelerinin Sınıflandırılması

The Breast cancer is the second cancer type which causes death of women. The premature detection of cancer and the suitable treatment applied to cancer cells can reduce the deadly risk. The medical doctors can make faults in diagnosis of the cancer disease. The performance of artificial intelligence methods (AIMs) containing increased thanks to rapid improvements in the technologies of the computer hardware. AIMs can be used regarding the enhancement of diagnostic accuracy. Standard Gradient–Based back propagation artificial neural networks (BP–ANN) has been commonly utilized in the diagnosis of the breast cancer disease. Even though BP–ANN are good performance in diagnosis of cancer disease, it has some limitations such as possible to be trapped in local minima and long time in the training process. In this study, the extreme learning machine assisted by heuristic firefly algorithm (FF–ELM) is proposed for diagnoses of breast cancer disease on the Breast Cancer Wisconsin Dataset. The diagnostic performance of proposed FF–ELM was compared with the standard ELM and BP–ANN methods. The results show that FF–ELM provides a meaningful enhancement regarding the classification performance and it can be used as a powerful technique for the medical problems.

BP-ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System.

Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understanding of electrochemical treatment without increasing the cost. The backpropagation artificial neural network (BP–ANN) model was applied to predict chemical oxygen demand (COD) removal efficiency and total energy consumption (TEC). Current density, pH, supporting electrolyte concentration, and oxidation–reduction potential (ORP) were used as input parameters in the 2-chlorophenol synthetic wastewater model. Prediction accuracy was increased by using particle swarm optimization coupled with BP–ANN to optimize weight and threshold values. The particle swarm optimization BP–ANN (PSO–BP–ANN) for the efficient prediction of COD removal efficiency and TEC for testing data showed high correlation coefficient of 0.99 and 0.9944 and a mean square error of 0.0015526 and 0.0023456. The weight matrix analysis indicated that the correlation of the five input parameters was a current density of 18.85%, an initial pH 21.11%, an electrolyte concentration 19.69%, an oxidation time of 21.30%, and an ORP of 19.05%. The analysis of removal kinetics indicated that oxidation–reduction potential (ORP) is closely correlated with the chemical oxygen demand (COD) and total energy consumption (TEC) of the electro-oxidation degradation of 2-chlorophenol in wastewater.

Predicting SuperDARN cross polar cap potential by applying regression analysis and machine learning

The cross polar cap potential (CPCP) is one of the primary parameters characterizing the electrodynamic feature of the high latitude ionosphere convection. In this study, we perform a comprehensive investigation of the Super Dual Auroral Radar Network (SuperDARN) CPCP, based on a large database of measurements from 1999 to 2009, and its relationship with various parameters of the solar wind, interplanetary magnetic field (IMF) and geomagnetic indices. Specifically, the IMF clock angle, the IMF Bz, the solar wind velocity, the plasma proton density, AE index, SymH index and Dst index are under consideration. According to the results of the correlation, the input parameters are selected and two models of the CPCP based on the multivariate regression analysis and Back Propagation Artificial Neural Network (BP ANN) algorithm are proposed respectively. The regression and BP ANN models are validated and the accuracy as well as the stability of the models is tested by using independent datasets. The result shows that the root mean square error (RMSE) between the measured and the model values ranges from 3.7 to 6.7 kV and the linear correlation coefficients are close to, or above 0.7. The ANN model is shown to have a better performance than the regression model.

Prediction of pK(a) values of neutral and alkaline drugs with particle swarm optimization algorithm and artificial neural network

A prediction model of pKa values of neutral and alkaline drugs based on particle swarm optimization algorithm and back propagation artificial neural network, called PSO–BP ANN, was established. PSO–BP ANN model was proposed using back propagation artificial neural network trained by particle swarm optimization algorithm, and used to predict the pKa values. The five parameters, including relative N atom number, Randic index (order 3), relative negative charge, relative negative charge surface area and maximum atomic net charge, were selected by particle swarm optimization algorithm and used as input variables of the model. The output variable in the proposed model was pKa values. The experimental results showed that the three layers (5–7–1) prediction model had a good prediction performance. The absolute mean relative error, root mean square error of prediction and square correlation coefficient were 0.5728, 0.0512 and 0.9169, respectively. The pKa values of neutral and alkaline drugs were positively correlated with the value of maximum atomic net charge, but the pKa value decreased with the increase in the other four parameters.

Comprehensive prediction method for die-roll height of fine-blanking components

This study proposes an economic and effective approach for predicting the die-roll height of fine-blanking components by fully considering the effect of three types of parameters: geometric features of components, process parameters of fine-blanking, and material parameters of fine-blanked sheets. Two prediction submodels that involved geometric features and process parameters individually were established based on a back-propagation artificial neural network (BP ANN), merged into one model, and finally generalised to different materials by correction according to experimental results. During modelling, simulations rather than experiments were used to obtain data for supporting BP ANN considering the demand for a single controlling variable and for saving economic and time costs. Analyses showed that geometric features had a more distinct influence on die-roll height; furthermore, the effect of process parameters is complicated and can be described more suitably by BP ANN. The prediction results of the combined model were favourable compared with the experimental results. Furthermore, the experimental curve used for correction during generalisation of the model showed that a material with higher yield strength generally had smaller die-roll.

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.

Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm

AbstractForest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong Province of East China. The experimental results show that the RBF model produces a relatively accurate biomass estimation compared with multivariate linear regression (MLR), k-Nearest Neighbor (KNN), and backpropagation artificial neural network (BP ANN) models.

Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region

Effective and accurate monitoring of grassland above-ground biomass (AGB) is important for pastoral agriculture planning and management. In this study, we combined 1433 AGB field measurements and remotely sensed data with the goal of establishing a suitable model for estimating grassland AGB in the Three-River Headwaters Region (TRHR) of China, which is one of the most sensitive regions to the warming climate. A back-propagation artificial neural network (BP ANN) was used to select the variables that contribute the most to the model's estimation of AGB, and then we built the model. Out of 13 variables, 5 variables were selected to build the BP ANN model, and we used cross validation for the accuracy assessment. The results show that: (1) the modeled mean AGB (2001–2016) provides a reasonable spatial distribution that is similar to the field measurements but reveals more details and has better spatial coverage than the limited field measurements are able to provide; (2) the overall trend of AGB in the TRHR is increasing more than decreasing (44.4% vs 29.2%, respectively) and has a stable area of 26.4%; and (3) the BP ANN model achieves better results than do the traditional multi-factor regression models (R2: 0.75–0.85 vs 0.40–0.64, RMSE: 355–462 vs 537–689kgDW/ha). This study presents an effective and operational BP ANN model that estimates grassland AGB for the study area with high accuracy at 500m spatial resolution, providing a scientific basis for the determination of reasonable stocking capacity and possible future development.

Artificial neural network application to microstructure design of Nb-Si alloy to improve ultimate tensile strength

In this paper, a feed forward neural network with back propagation artificial neural network (BP ANN) was developed to predict ultimate tensile strength (UTS) and optimize microstructure. The alloys were produced by directional solidification and heat treatment. The UTS was measured for ANN output. Five characteristic factors used for ANN input were abstracted and measured. As the result of this study, the ANN model with high accuracy and good generalization ability to predict UTS within the range of 343.5–1063.3MPa was established and mutual verified with sensitivity analysis. Based on the optimized ANN model, a new way to design microstructure of Nb-Si alloy to obtain required UTS was proposed. With silicide design maps made by ANN model, the microstructure of the sample of 343.5MPa was optimized and the UTS reached the target UTS (600MPa) successfully.

Artificial neural network application to study quantitative relationship between silicide and fracture toughness of Nb-Si alloys

In this paper, a forward and backward feed propagation artificial neural network (BP ANN) was developed to research the quantitative relationship between silicide and fracture toughness of Nb-Si alloys. The alloys were produced by directional solidification and heat treatment. The toughness was measured by a three-point bending method used for ANN output. Five characteristic factors used for ANN input were abstracted and measured. The sequence of factors is silicide volume fraction>γ-Nb5Si3>silicide shape>silicide size>silicide continuity by sensitivity analysis. As a result of this study, the ANN model was found to be successful for predicting toughness with high accuracy and good generalization ability within the range of 9.2–26.1MPam1/2. The quantitative formulas of silicide feature parameters and fracture toughness were established by transfer function, weight matrix and threshold of ANN model. The effect of each parameter and interact influence of two parameters on the fracture toughness were studied, and the technological parameters of the alloy were optimized by artificial neural network model.

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a “Weights” method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation.

The Abnormal Network Traffic Recognition Method Based on Optimized BP ANN Model

To recognize abnormal traffic in network, so as to perceive the illicit behavior in network, carry out scientific and effective management, and ensure the network security, we extracted the abnormal network traffic features and proposed an abnormal network traffic recognition method based on optimized Back Propagation Artificial Neural Networks (BP ANN). The experimental results indicate that, although the training time is longer, but the accuracy rate of BP ANN in abnormal network traffic identification is superior to other methods. And the convergence rate of optimized BP ANN model is significantly faster than traditional BP ANN model.

The Abnormal Network Traffic Recognition Method Based on Optimized BP ANN Model

The Abnormal Network Traffic Recognition Method Based on Optimized BP ANN Model

Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

Abstract. Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009–2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson’s correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

GA-BP神经网络结合PCA的多基色颜色预测模型

Color partition method is the main method of multi-color reproduction. In this paper,the whole color area of 7 primary colors was divided into 6 partitions in group of 3 primary colors,and principal component analysis( PCA) was used to dimensionality reduction of the color spectrum reflectance. A three layers BP neural network was established to describe the transformation model of dot area coverage and spectral reflectance. Genetic algorithm( GA) was adopted for optimizing the weight threshold of BP NN to improve the prediction accuracy of model. Experimental results show that GA-BP model has higher prediction precision and stability compared with BP ANN and cell neugebauer model. When the number of training sample is 64 and test sample is 216 in the partition,the optimized model can predict color with the accuracy of 1. 669 mean ΔE*aband 0. 7% spectral RMSE. By comparing with the non-optimized model with the training sample numbers of 125,216,343( mean ΔE*abare 3. 267,2. 776,2. 175 and spectral RMSE are 0. 97%,0. 79%,0. 76%),the prediction accuracy of GA-BP model with the training sample numbers of 64 is equal to the accuracy of the non-optimized model with the training sample number of 343. The results show that GA-BP model can reach high precision color reproduction with small amount of samples,andhave good portability in practice.

A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries

Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery’s kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries.

Real time optimization based on a serial hybrid model for gold cyanidation leaching process

To implement optimization and control on gold cyanidation leaching process (GCLP), it is an important prerequisite to establish an accurate process model. In this paper, a hybrid model in serial structure was proposed, where a first-principle model based on mass conservation equations was presented to describe the basic process behavior and its unknown kinetic reaction rates were predicted using BP ANN models without any structures considered. The proposed serial hybrid model had been applied to the prediction of gold recovery of the GCLP in a gold treatment plant. The results indicate that the proposed serial hybrid model has better prediction performance and generalization ability than the pure mechanistic model. To further reduce the effect of prediction error (plant-model mismatch) on real time optimization (RTO), modifier adaptation approach had been investigated and implemented to the GCLP. The result shows that when model mismatches with the actual plant or larger process disturbance occurs, significant reduction of production cost can be actualized iteratively by implementing the proposed adaptive RTO strategy.

Artificial neural network modelling to predict hot deformation behaviour of as HIPed FGH4169 superalloy

The hot deformation behaviour of as HIPed FGH4169 superalloy was studied by single stroke compression test on MMS-200 test machine at the temperatures of 950–1050°C and the strain rates of 0·004–10 s−1. Based on the experimental results, a back-propagation artificial neural network model and constitutive equation method were established to predict the flow stress of FGH4169 superalloy. The predictability of two different models was compared. The correlation coefficients of experimental and predicted flow stress with the trained BP ANN model and constitutive equation were 0·9995 and 0·9808 respectively. The average root mean square error (RMSE) values of the trained ANN model and constitutive equation are 0·39 and 2·21 MPa respectively. And the average absolute relative error (AARE) values of the trained ANN model and constitutive equation are 1·79 and 7·47% respectively. The results showed that the ANN model is an effective tool to predict the flow stress in comparison with constitutive equation.

SLOPE STABILITY ANALYSIS OF KALLAR-COONOOR HILL ROAD STRETCH OF THE NILGIRIS

The stability of slopes is always under severe thre ats in many parts of Western Ghats, especially in K allarCoonoor hill road stretch, causing disruption, loss of human life and economy. To minimize the instability of soil slope in between Kallar-Coonoor, a critical evaluation of roads is required. The stability of slopes depends on the soil shear strength parameters such as Cohesion, Angle of internal friction, Unit weigh t of soil and Slope geometry. The stability of a slope i s measured by its factor of safety using geometric and shear strength parameter based on infinite slopes. In this present study, investigation was carried ou t at 32 locations in the above said hill road stretch to es timate the factor of safety of the slope determined by MohrCoulomb theory based on shear strength parameter calculated from direct shear test which is a conventi onal procedure for this study. Back Propagation Artifici al Neural Network (BP-ANN) Model is used to predict the factor of safety. The input parameters for the (BP-ANN) are chosen as Cohesion, Angle of internal friction, Density and Slope angle and the factor of safety as output. Out of the parameters of 32 loca tions, the study of BP-ANN is trained with parameters of f irst 25 locations. Factor of safety was calculated for the remaining 7 locations. The results obtained in BP-A NN method were compared with that of conventional method and observed a good agreement between these two methods. The results obtained from these two methods were also compared with the details of actu al field Landslide occurred and indicates 71.4% of conventional method locations matching with the physical occurrences and 85.7% of BP-ANN predicted vulnerable locations match with the physically obse rved landslide locations.