Structure Of Back Propagation Neural Network Research Articles

Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based-BP Neural Network

In order to predict the relative dynamic elastic modulus (RDEM), which is used to reflect the frost resistance of iron ore tailings concrete, the backpropagation neural network (BPNN) was used in this study. Here, one hidden layer was chosen in the structure of BPNN. It is well known that the number of neurons in the hidden layer is the key of BPNN; hence, checking the features of overfitting was chosen in this paper to determine the number of neurons in the hidden layer. According to the actual conditions of freeze–thaw cycle test, a BPNN model of 2D input vector and 1D output vector was established. Thirty datasets from the test were used for training and test the proposed ANN. The results showed that the predicted values were in good agreement with the measured ones, and the correlation coefficient between them reached 0.9505. It showed that the BPNN, with the ability to solve nonlinear problems, had great advantages in predicting RDEM, and it can be used as reliable and simple forecasting tools for the prediction of frost resistance of iron ore tailings concrete.

Modeling and semi-active control performance analysis of MRE isolator based on BPNN optimized by GA using different evaluation indexes

The utilization of isolation systems employing Magnetorheological Elastomer (MRE) devices holds significant promise for structural vibration applications due to their customizable stiffness and damping characteristics. However, the nonlinear dynamics inherent in MRE isolators present formidable obstacles for the establishment of accurate models and development of effective control strategies for practical implementation. In this work, the dynamic properties of a self-made MRE isolator under different loading conditions are tested and analyzed. Then the nonparametric forward model and inverse model of MRE isolator based on BPNN (back propagation neural network) are established respectively, and the GA (genetic algorithm) is used to optimize the neural structure of BPNN. The precision and accuracy of the forward and inverse model is verified by comparing the predicted and experimental data. Simulation and experimental results show that the BPNN optimized by GA can efficiently and accurately model the nonlinear behavior of MRE isolators. Based on this, we take an eight-story shear frame building based on the proposed MRE vibration isolator model as the research object and numerically studied the vibration suppression effect under the control of three typical control algorithms, that is, LQR, FC, and FC-PID. In the process of evaluating the effect of vibration isolation control, in addition to utilizing the traditional displacement amplitude, layer distance and acceleration as evaluation indexes, we also propose a new comprehensive evaluation index which has weighting coefficients and considers the input cost. It is shown that the newly proposed comprehensive index can more conveniently compare the advantages and disadvantages of different control algorithms, and the fuzzy PID is the most suitable among the three control algorithms.

BP neural network to predict shelf life of channel catfish fillets based on near infrared transmittance (NIT) spectroscopy

The objective of this research was to establish shelf-life prediction model of channel catfish fillets by Back-propagation (BP) neural network technology based on near infrared transmittance (NIT). First, freshness prediction model of channel catfish fillets was established based on the chemical analysis data (total volatile basic nitrogen (TVB-N), K value, thiobarbituric acid reactive substance (TBARS) and trimethylamine (TMA)) and NIT spectra (850–1050 nm). The linear correlation coefficient (R2: 0.667–0.887) showed a good performance of the freshness model prediction. Then, BP neural network was applied to establish the shelf-life prediction model of catfish fillets under temperature fluctuation (−6 to −18 °C). The end effective accumulated temperature of frozen catfish fillets was 10,278.4 h °C. The prediction model showed a great stability (above 93 %) and accuracy (above 90 %) as the structure of BP neural network was 4–7–1. Therefore, this study provided a practical basis and technical supports for the establishment of shelf-life prediction model of freshwater fillets by BP neural network based on NIT spectroscopy.

Neural Network Model Design for Landscape Ecological Planning Assessment Based on Hierarchical Analysis.

In this paper, an in-depth study and analysis of landscape ecological planning and evaluation are carried out using the analytic hierarchy process (AHP) algorithm that integrates neural networks. The application of AHP in the field of tree species planning and the introduction of quantitative analysis methods can effectively change the subjectivity of previous qualitative analysis in tree species selection and make it objective, scientific, and reasonable. The research can provide a reference for other urban tree species planning. From the connotation of landscape ecological service process and ecological space structure, the analysis of landscape ecological service process involves service supply area and service association area, which correspond to different key components of ecological space structure. With the help of the platform, based on the identification and identification methods and theories of ecological spatial structure, the key components of ecological spatial structure in different environments are identified and extracted by using the representation model, binary suitability model, weighted suitability model, and process model. The type of service is based on the different service processes supported by the key components of the ecological spatial structure, forming the ecological spatial structure under different service types. Spatial structure; on this basis, the basic characteristics of the key components of the ecological spatial structure are analyzed, and the correlation characteristics of the ecological spatial structure are analyzed based on the correlation classification system of ecological spatial structure. A backpropagation (BP) neural network-based state assessment method of the grid structure is established. The method takes the parameters of the autoregressive model constructed by the acceleration signals of different working conditions as the feature quantity and the results of the fuzzy hierarchical analysis method as the labels, divides the data set into a training set and a test set, and uses the BP neural network learning method and the training set to supervised train the BP neural network learning assessment model. The test set is used to test the effectiveness and accuracy of the BP neural network-based learning method. The study shows that the evaluation system established by the BP neural network structure is fast and accurate and can substantially reduce the cost of manual testing.

PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE

Reducing the physical and mental weariness of drivers is significant in improving healthy and safe driving. This paper is aim to predict the stress level of drivers while braking in various conditions of the track. By discovering the drivers’ mental stress level, we are able to safely and comfortably adjust the distance in relation to the vehicle ahead. The initial step used was a study related to Artificial Intelligence (AI), Electroencephalogram (EEG), safe distance in braking, and the theory of mental stress. The data was collected by doing a direct measurement of drivers’stress levels using the EEG tool. The respondents were 5 parties around 30-50 years old who had experience in driving for> 5 years. The research asembled 400 pieces of data about braking including the data of the velocity before braking, track varieties (cityroad, rural road, residential road, and toll road), braking distance, stress level (EEG), and focus (EEG). The database constructed was used to input the machine learning (AI) – Back Propagation Neural Network (BPNN) in order to predict the drivers’ mental stress level. Referring to the data collection, each road type gave a different value of metal stress and focus. City road drivers used an average velocity of 23.24 Km/h with an average braking distance of 11.17 m which generated an average stress level of 53.44 and a focus value of 45.76.Under other conditions, city road drivers generated a 52.11 stress level, the rural road = 48.65, and 50.23 for the toll road. BPNN Training with 1 hidden layer, neuron = 17, ground transfer function, sigmoid linear, and optimation using Genetic Algorithm (GA) obtained the Mean Square Error (MSE) value = 0.00537. The road infrastructure, driving behavior, and emerging hazards in driving took part in increasing the stress level and concentration needs of the drivers. The conclusion may be drawn that the available data and the chosen BPNN structure were appropriate to be used in training and be utilized to predict drivers’ focus and mental stress level. This AI module is beneficial in inputting the data to the braking car safety system by considering those mental factors completing the existing technical factor considerations.

A Novel Music Emotion Recognition Model Using Neural Network Technology.

Music plays an extremely important role in people’s production and life. The amount of music is growing rapidly. At the same time, the demand for music organization, classification, and retrieval is also increasing. Paying more attention to the emotional expression of creators and the psychological characteristics of music are also indispensable personalized needs of users. The existing music emotion recognition (MER) methods have the following two challenges. First, the emotional color conveyed by the first music is constantly changing with the playback of the music, and it is difficult to accurately express the ups and downs of music emotion based on the analysis of the entire music. Second, it is difficult to analyze music emotions based on the pitch, length, and intensity of the notes, which can hardly reflect the soul and connotation of music. In this paper, an improved back propagation (BP) algorithm neural network is used to analyze music data. Because the traditional BP network tends to fall into local solutions, the selection of initial weights and thresholds directly affects the training effect. This paper introduces artificial bee colony (ABC) algorithm to improve the structure of BP neural network. The output value of the ABC algorithm is used as the weight and threshold of the BP neural network. The ABC algorithm is responsible for adjusting the weights and thresholds, and feeds back the optimal weights and thresholds to the BP neural network system. BP neural network with ABC algorithm can improve the global search ability of the BP network, while reducing the probability of the BP network falling into the local optimal solution, and the convergence speed is faster. Through experiments on public music data sets, the experimental results show that compared with other comparative models, the MER method used in this paper has better recognition effect and faster recognition speed.

Modelling the hardened properties of steel fiber reinforced concrete using ANN

The hardened properties of concrete are one of the most important parameters when considering the sustainability criteria of buildings. Numerous previous studies have established how the Artificial Neural Network is used as an effective statistical data modelling tool. In this study, the prediction of Elastic modulus and Compressive Strength of steel fibre reinforced concrete have been illustrated with a feed-forward backpropagation neural network structure. 158 datasets are used for modelling of elastic modulus and 140 datasets is used for modelling of Compressive Strength. The developed ANN models were able to predict the data within the range of input parameters considered and were having regression coefficients values of 0.96 and 0.97 respectively. A comparison of actual and predicted values has been performed and the results indicated that the ANN performed better in terms of prediction with minimum deviation from original data.

Research on improved convolutional wavelet neural network

Artificial neural networks (ANN) which include deep learning neural networks (DNN) have problems such as the local minimal problem of Back propagation neural network (BPNN), the unstable problem of Radial basis function neural network (RBFNN) and the limited maximum precision problem of Convolutional neural network (CNN). Performance (training speed, precision, etc.) of BPNN, RBFNN and CNN are expected to be improved. Main works are as follows: Firstly, based on existing BPNN and RBFNN, Wavelet neural network (WNN) is implemented in order to get better performance for further improving CNN. WNN adopts the network structure of BPNN in order to get faster training speed. WNN adopts the wavelet function as an activation function, whose form is similar to the radial basis function of RBFNN, in order to solve the local minimum problem. Secondly, WNN-based Convolutional wavelet neural network (CWNN) method is proposed, in which the fully connected layers (FCL) of CNN is replaced by WNN. Thirdly, comparative simulations based on MNIST and CIFAR-10 datasets among the discussed methods of BPNN, RBFNN, CNN and CWNN are implemented and analyzed. Fourthly, the wavelet-based Convolutional Neural Network (WCNN) is proposed, where the wavelet transformation is adopted as the activation function in Convolutional Pool Neural Network (CPNN) of CNN. Fifthly, simulations based on CWNN are implemented and analyzed on the MNIST dataset. Effects are as follows: Firstly, WNN can solve the problems of BPNN and RBFNN and have better performance. Secondly, the proposed CWNN can reduce the mean square error and the error rate of CNN, which means CWNN has better maximum precision than CNN. Thirdly, the proposed WCNN can reduce the mean square error and the error rate of CWNN, which means WCNN has better maximum precision than CWNN.

SPEECH RECOGNITION APPLICATION AS AN ANIMATED OBJECT MOVEMENT CONTROLLER SYSTEM

Technological developments in the world have no boundaries. One of them is Speech Recognition. At first, words spoken by humans cannot be recognized by computers. To be recognizable, the word is processed using a specific method. Linear Predictive Coding Method (LPC) is a method used in this research to extract the characteristics of speech. The result of the LPC method is the LPC coefficient which is the number of LPC orders plus 1. The LPC coefficient is processed using Fast Fourier Transform (FFT) 512 to simplify the process of speech recognition. The results are then trained using Backpropagation Neural Network (BPNN) to recognize the spoken word. Speech recognition on the program is implemented as an animated object motion controller on the computer. The end result of this research is animated objects move in accordance with the spoken word. The optimal BPNN structure in this research is to use traingda training function, number of nodes 3, learning rate 0.05, epoch 1000, performance goal 0,00001. This structure can produce the smallest MSE value that is 0,000009957. So, this structure can recognize new words with 100% accuracy for trained data, 80% for the same respondents with trained data and reach 67.5% for new respondents.

Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network.

Recycled aggregate concrete (RAC), due to its high porosity and the residual cement and mortar on its surface, exhibits weaker strength than common concrete. To guarantee the safe use of RAC, a compressive strength prediction model based on artificial neural network (ANN) was built in this paper, which can be applied to predict the RAC compressive strength for 28 days. A data set containing 88 data points was obtained by relative tests with different mix proportion designs. The data set was used to develop an ANN, whose optimal structure was determined using the trial-and-error method by taking cement content (C), sand content (S), natural coarse aggregate content (NCA), recycled coarse aggregate content (RCA), water content (W), water–colloid ratio (WCR), sand content rate (SR), and replacement rate of recycled aggregate (RRCA) as input parameters. On the basis of different numbers of hidden layers, numbers of hidden layer neurons, and transfer functions, a total of 840 different back propagation neural network (BPNN) models were developed using MATLAB software, which were then sorted according to the correlation coefficient R2. In addition, the optimal BPNN structure was finally determined to be 8–12–8–1. For the training set, the correlation coefficient R2 = 0.97233 and RMSE = 2.01, and for the testing set, the correlation coefficient R2 = 0.96650 and RMSE = 2.42. The model prediction deviations of the two were both less than 15%, and the results show that the ANN achieved pretty accurate prediction on the compressive strength of RAC. Finally, a sensitivity analysis was carried out, through which the impact of the input parameters on the predicted compressive strength of the RAC was obtained.

Application of fractional theory in quantum back propagation neural network

In this paper, by applying the theory of fractional calculus to quantum back propagation (BP) neural network, a quantum BP algorithm based on the definition of fractional Grünwald–Letnikoff (G‐L) is proposed. We choose the Sigmoid linear superposition function to replace the activation function of the traditional neural network to construct a fractional quantum BP neural network structure. Experimental results prove that this algorithm improves the convergence speed of the network and reduces the convergence error.

ON-LINE DETECTION OF CERASUS HUMILIS FRUIT BASED ON VIS/NIR SPECTROSCOPY COMBINED WITH VARIABLE SELECTION METHODS AND GA-BP MODEL

In order to realize the rapid and non-destructive detection of fresh Cerasus Humilis’ (CH) classification, and promote the deep-processing of post-harvest fresh fruit and improve market competitiveness, this study proposed a nonlinear identification method based on genetic algorithm (GA) optimized back propagation (BP) neural network of different varieties of fresh CH fruit. “Nongda-4”, “Nongda-5”, and “Nongda-7” fresh CH fruit were selected as research objects to collect their visible/near-infrared spectral data dynamically. The original spectra were preprocessed by moving smoothing (MS) and standard normal variate (SNV) methods, for the characteristic wavelengths were extracted with four dimension-reducing methods, namely principal components analysis (PCA), competitive adaptive reweighed sampling (CARS), CARS-mean impact value (CARS-MIV), and random frog (RF) algorithm. Finally, the BP prediction models were established based on full-spectrum and characteristic wavelengths. At the same time, the GA optimization was used to optimize the initial weight and threshold of the BP neural network and compared with the partial least squares’ discrimination analysis (PLS-DA) linear model. Through comparing the MS (7)+SNV was proved to be the best preprocessing method, the CARS-MIV-GA-BP model had the best discriminant accuracy, the prediction set accuracy was 98.76%, of which the variety “Nongda-4” and “Nongda-5” recognition rate were 100%, the variety “Nongda-7” recognition rate was 96.29%. The results show that the GA can effectively optimize the initial weights and threshold randomization of the BP neural network, improve the discrimination accuracy of CH varieties, and the CARS-MIV algorithm can effectively reduce the number of input nodes of the BP neural network model, simplify the structure of BP neural network. This study provides a new theoretical basis for the detection of fresh CH fruit classification.

A neural network-based prediction model in water monitoring networks

Abstract To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm–back propagation (ISOA–BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks.

Evaluation of the use of sublevel open stoping in the mining of moderately dipping medium-thick orebodies

The flow of blasted ore during mining of moderately dipping medium-thick orebodies is a challenge. Selecting a suitable mining system for such ore bodies is difficult. This paper proposes a diamond layout sublevel open stoping system using fan blastholes with backfilling to mine such orebodies. To evaluate the performance of system the relationships between ore recovery and stope footwall dip angle, footwall surface roughness, drawpoint spacing and production blast ring burden were investigated. An ore recovery data set from 81 laboratory physical model experiments was established from combinations of the listed factors. Various modules in a back propagation neural network structure were compared, and an optimal network structure identified. An ore recovery backpropagation neural network (BPNN) forecast model was developed. Using the model and sensitivity analysis of the factors affecting the proposed open stope mining system, the significance of each factor on ore recovery was studied. The study results were applied to a case study at the Shandong Gold Group Jiaojia Gold Mine. The results showed that the application of a BPNN and sensitivity analysis models for ore recovery prediction in the proposed mining system and field experimental results confirm that the suggested mining method is feasible.

Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings

Sparsity of rating data is a severe problem to be solved in modern recommendation researches. The fusion recommendation method is an effective solution for the problem. The method combines rating data and other types of user feedback data, such as reviews and image, to improve performance of the traditional recommendation algorithms. Some researchers have proposed fusion recommendation algorithms based on BP (Back Propagation) neural network and achieved better results. However, some existing fusion recommendation algorithms based on BP neural network still have some shortcomings. They rely on the assistance of the traditional recommendation algorithms. Moreover, the high complexity of the fusion processes of these algorithms possibly has negative impacts on the fusion effects. In this paper, we modify the fusion recommendation algorithm and propose the NNFR (neural networks fusion recommendation) model. This model improves the structure of BP neural network by specially designing the structure of network layers. User reviews and ratings can be processed in two separate sub-networks respectively and further fused in the fusion layer. The fusion features of user reviews and ratings are directly applied to perform recommendation, in order to avoid the assistance of the traditional recommendation algorithms and improve the fusing efficiency and quality. Experimental results indicate that the outstanding performance of NNFR model than comparative recommendation algorithms on rating predictions and top-k recommendations. Moreover, NNFR model can still produce high-quality recommendation results in the scenarios of sparse data.

Modeling Height–Diameter Relationship for Poplar Plantations Using Combined-Optimization Multiple Hidden Layer Back Propagation Neural Network

Relationship of total height and diameter at breast height (hereafter diameter) of the trees is generally nonlinear, and therefore has complex characteristics, which can be accurately described by the height-diameter model developed using the back propagation (BP) neural network approach. The multiple hidden layered-BP neural network has several hidden layers and neurons, and is therefore considered more appropriate modeling approach compared to the single hidden layered-BP neural network approach. However, the former approach is not widely applied for tree height prediction due to absence of the effective optimization method, but it can be done using the BP neural network modeling approach. The poplar (Populus spp. L.) plantation data acquired from Guangdong province of China were used for evaluating the BP neural network modeling approach and compared its results with those obtained from the traditional regression modeling and mixed-effects modeling approaches. We determined the best BP neural network structure with two hidden layers and five neurons in each layer, and logistic sigmoid transfer functions. Relative to the Mitscherlich height-diameter model that had the highest fitting precision among the six traditional height-diameter models evaluated, coefficient of determination (R2) of the neural network height-diameter model increased by 10.3%, root mean squares error (RMSE) and mean absolute error (MAE) decreased by 12% and 13.5%, respectively. The BP neural network height-diameter model also appeared more accurate than the mixed-effects height-diameter model. Our study proposes the method of determining the optimal numbers of hidden layers, neurons of each layer, and transfer functions in the BP neural network structure. This method can be useful for other modeling studies of similar or different types, such as tree crown modeling, height, and diameter increments modeling, and so on.

Nonlinear system modeling and application based on restricted Boltzmann machine and improved BP neural network

Aiming at the complexity, nonlinearity and difficulty in modeling of nonlinear system. In this paper, an improved back-propagation(BP) neural network based on restricted boltzmann machine(RBM-IBPNN) is proposed for nonlinear systems modeling. First, the structure of BP neural network(BPNN) is optimized by using sensitivity analysis(SA) and mutual information(MI) of the hidden neurons. Namely when the SA value and the MI value of the hidden neurons satisfy the set standard, the corresponding neurons will be pruned, split or merged. second, the restricted boltzmann machine(RBM) is employed to perform parameters initialization of training on the IBPNN. Finally, the proposed RBM-IBPNN is evaluated on nonlinear system identification, lorenz chaotic time series prediction and the total phosphorus prediction problems. The experimental results demonstrate that the proposed RBM-IBPNN not only has faster convergence speed and higher prediction accuracy, but also realizes a more compact network structure.

Forecasting Power Output of Photovoltaic System Using A BP Network Method

The characteristics of intermittent and stochastic of solar energy has brought great challenges to power grid system in terms of operation and regulation. Power forecasting is an important factor for optimal schedule of power grid system and assessing the working performance of PV systems. In order to forecast the power output of a PV system located in Ashland at 24-hour-ahead for higher efficiency, a back propagation (BP) neural network model is proposed. Before designing the model, correlation analysis is done to investigate the relationship between power output and solar irradiance and ambient temperature, which are key parameters affecting the power output of PV systems. Based on a correlation analysis, the model admitted the following input parameters: hourly solar radiation intensity, the highest, the lowest daily and the average daily temperature, and hourly power output of the PV system. The output of the model is the forecasted PV power output 24 hours ahead. Based on the datasets, the neural network is trained to improve its accuracy. The best performance is obtained with the BP neural network structure of 28-20-11. The analysis of the error indicator MAPE shows that the proposed model has great accuracy and efficiency for forecasting the power output of photovoltaic systems.

Selection of criteria for multi-criteria decision making of reservoir flood control operation

In reservoir flood control operation, selection of criteria is an important part of the multi-criteria decision making (MCDM) procedure. This paper proposes a method to select criteria for MCDM of reservoir flood control operation based on the back-propagation (BP) neural network. According to the concept of ideal and anti-ideal points, we propose a method to generate training samples of the BP neural network via stochastic simulation. The topological structure of a three-layer BP neural network used for criteria selection is established. The relative importance of criteria is derived via the learned connection weights of a trained BP neural network, and its calculation method is proposed. The sensitivity curve method is employed to conduct sensitivity analysis, and the relative contribution ratio is defined to quantify the relative sensitivity strength of each criterion. We present the principle and threshold value of criteria selection based on the comprehensive discrimination index defined by the combination of the relative importance and relative contribution ratio. The Pubugou reservoir is selected as the case study. The results show that the proposed method can provide an effective tool for decision makers to select criteria before MCDM modeling of reservoir flood control operation.

A modeling method based on artificial neural network with monotonicity knowledge as constraints

This paper proposes a new strategy to model a nonlinear industrial process. The proposed approach is based on Back-Propagation Neural Network (BPNN) models combined with monotonicity knowledge from mechanism analysis or expertise. The main contribution is to construct a unified framework for the modeling process by integrating the BPNN, monotonicity detection, constraint optimization problem, and Evolutionary Algorithm (EA). Initially, a BPNN structure is chosen to describe the input–output relationship. In the training process of the BPNN, the monotonicity knowledge is used as constraint condition for modeling. Thus, the training process changes to a Constraint Optimization Problem (COP). Finally, a new EA, namely Alopex-based EA (AEA), is used to search for the optimal weights and thresholds. This novel approach can avoid the over-fitting problem to some extent, especially when modeling a system with a small quantity of samples. In addition, we explain this method by the Bayesian inference and apply the method to model the acetylene concentration at the outlet of an acetylene hydrogenation reactor.