Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction

With the gradual warming of the global climate, wind energy, as one of the representatives of clean energy, has been rapidly developed in countries worldwide. However, wind power generation has strong intermittency and variability, constantly impacting the stability of the power grid. In order to improve the accuracy of wind power prediction, this paper proposes a BP neural network model optimized by genetic algorithm (GA) for wind power prediction. This model uses GA to optimize the initial weights and thresholds of the BP neural network to improve the computational accuracy and convergence speed of the model, effectively avoiding the problem of a single BP neural network model easily falling into local minima and the slow training speed of GA algorithm. By preprocessing and normalizing meteorological data such as weather, temperature, and wind direction, a wind power prediction model is constructed, and the prediction results show that the model effectively improves the computational accuracy and convergence speed compared to the traditional BP neural network model. This result proves the feasibility of using GA to optimize BP neural network for wind power prediction in this paper.

Fatigue driving has always received a lot of attention, but few studies have focused on the fact that human fatigue is a cumulative process over time, and there are no models available to reflect this phenomenon. Furthermore, the problem of incorrect detection due to facial expression is still not well addressed. In this article, a model based on BP neural network and time cumulative effect was proposed to solve these problems. Experimental data were used to carry out this work and validate the proposed method. Firstly, the Adaboost algorithm was applied to detect faces, and the Kalman filter algorithm was used to trace the face movement. Then, a cascade regression tree-based method was used to detect the 68 facial landmarks and an improved method combining key points and image processing was adopted to calculate the eye aspect ratio (EAR). After that, a BP neural network model was developed and trained by selecting three characteristics: the longest period of continuous eye closure, number of yawns, and percentage of eye closure time (PERCLOS), and then the detection results without and with facial expressions were discussed and analyzed. Finally, by introducing the Sigmoid function, a fatigue detection model considering the time accumulation effect was established, and the drivers’ fatigue state was identified segment by segment through the recorded video. Compared with the traditional BP neural network model, the detection accuracies of the proposed model without and with facial expressions increased by 3.3% and 8.4%, respectively. The number of incorrect detections in the awake state also decreased obviously. The experimental results show that the proposed model can effectively filter out incorrect detections caused by facial expressions and truly reflect that driver fatigue is a time accumulating process.

In the design of the bridge reliability assessment proposal, the application of the BP neural network model can help overcome some shortcomings in the traditional bridge reliability assessment, such as the poor model adaptability, the low calculation efficiency and so on. However, there are also some problems in the BP neural network model, for example, the uneasily determinable initial weights and easily local optimum. The BP neural network model optimized by the application of GA global optimum characteristics can get the optimum relation quickly. Therefore, this paper puts forward a GA-BP neural network model based on the real number coding system to analyze the bridge reliability assessment and applies it to the reliability assessment in Masangxi Yangtze River Bridge. The results of the application show that the GA-BP neural network model has a higher accuracy, higher reliability and application value in the assessment of the engineering works than the traditional BP neural network model.

Real estate has been an important part of the Chinese economy. It is of great significance to study the factors influencing commercial, residential housing prices for stabilizing economic growth. In this paper, we designed a new index set according to the principle of economics. On this basis, we proposed a Back Propagation neural network model based on genetic algorithm optimization. The experimental results show that the improved BP neural network based on Genetic Algorithm has a better fitting effect than the traditional fitting model and BP neural network model, and MSE decreases by 35.6% and 14.5%, respectively. Through the improved model, we can reference the study of housing prices, real estate buyers, and investors' decision-making.

In view of the complex structure of the fire control system, difficulty in obtaining fault information, and multiple fault characteristics. This paper proposes a fire control system fault diagnosis method based on genetic algorithm optimized BP neural network. For the problem of poor prediction accuracy of BP neural network in the operation process, and easy to fall into local extreme value. In order to obtain a better diagnosis effect, the principle of complementary advantages is used to combine genetic algorithm with BP neural network algorithm. The genetic algorithm is used to calculate the initial values of network parameters, optimize the initial weights and thresholds, and find the optimal number of hidden layer nodes. Combined with case analysis, the accuracy of this method is improved compared with traditional BP neural network in solving the problem of fire control system fault diagnosis. At the same time, it proves the effectiveness of the proposed method in fire control system fault diagnosis.

The sparrow search algorithm (SSA) has received widespread attention as an emerging group intelligence algorithm. In this study, a QSAR (quantitative structure-activity relationship) prediction model for anti-ERa (Estrogen receptor alpha, an important target for the treatment of breast cancer) compounds and their bioactivity data was constructed based on a sparrow search algorithm optimized BP neural network combined with the selected relevant parameter indicators. Then the created model was used to predict the biological activity of the compounds. The results show that the created model not only has a certain self-learning function, but also improves the convergence speed and the accuracy of the prediction results compared with the BP neural network model optimized by genetic algorithm (which is more complex in coding and slower in solving, but has good solution accuracy). Further, it was demonstrated that the use of SSA-BP could improve the prediction of QSAR for anti-ERa compounds.

Sound quality evaluation may be conducted with multiple different objective evaluation models, but reliability of evaluation results is different.Noise samples of two ears of the driver were gathered when the car operated at the speed of 60km/h and different music was played in the car.After equal loudness, frequency band and filtering treatment of sound samples, subjective evaluation of sound quality was carried out.Based on calculation of objective parameters, MLR (Multiple Linear Regression) objective evaluation model for sound quality, objective evaluation model for sound quality based on BP neural network and objective evaluation model for sound quality based on RBF (radial basis function) neural network were established.The test samples were substitute into the three models, and their sound quality prediction effects at each frequency band were compared.The results show that MLR model is simple and has fast calculation speed, so it is suitable for linear problem analysis; BP neural network model is suitable for solving large sample problem with complex internal mechanism; RBF neural network model has high precision and small relative error, so it is suitable for analysis of nonlinear problem of small sample.Under experimental conditions, prediction effect of RBF model is superior to MLR model and BP neural network model.

In view of the actual situation in the port of Tianjin, to overcome the traditional BP neural network method in the prediction model, such as low efficiency and poor accuracy of faults, the paper puts forward a kind of prediction model based on principal component analysis (PCA) and BP neural network. In this approach, use PCA to reduce the dimension of the original data, so as to achieve the purpose of simplifying the network structure. The calculation results of SPSS and MATLAB software show that PCA-BP neural network prediction model compared to traditional BP neural network model can improve the prediction efficiency and improve accuracy. Therefore, this method is more suitable for predicting the throughput of the port of Tianjin, and it has a strong practical significance.

This paper discusses correlative relation between company's financial risk and data mining and studies currently financial crisis warning optimization method of listed company. It analyzes and demonstrates neural network input dimension optimization of rough intension simplification, network weights, and threshold value of genetic algorithm optimization. In addition, it empirically analyzes the optimized model and traditional BP neural network model. Then, the genetic algorithm is used as preset device of neural network model which optimizes the initial value and threshold value at input terminal, to shorten the training time and to improve network predictive efficiency. Empirical research shows that financial risk predictive accuracy of optimized model is higher than traditional predictive accuracy and efficiency of BP neural network model.

In this paper, a hybrid model (BP-GA) including a back propagation network and a genetic algorithm is used to estimate the thermal conductivity of the nanofluids. The genetic algorithm is used to optimize the initial weight and threshold of BP neural network, so that the optimized BP neural network can better predict the function output. In this study, CuO-ZnO hybrid nanofluids with the mass fraction of 0%, 1%, 2%, and 3% were studied at temperatures of 25, 30, 35, 40, 45, 50, 55, and 60 °C, respectively. The thermal conductivity of the hybrid nanofluids base liquid is 20:80, 40:60, 50:50, 60:40, 80:20, and the mass ratio of CuO-ZnO is 50:50. The mass fraction of CuO-ZnO, the temperature and the mixing ratio of different base liquids were used as input parameters, and the thermal conductivity was the output parameter, forming a 3-input ANN neural network. The predicted thermal conductivity results of BP neural network and genetic algorithm optimized BP neural network (GA-BP) were compared with experimental data. The results illustrate that the BP neural network optimized by genetic algorithm exerts a positive influence on the accuracy and stability of the predicted output.

Prediction Model of Transpiration Rate of Strawberry in Closed Cultivation Based on DBN-LSSVM Algorithm

Scraper conveyor load prediction is crucial to realize the cooperative speed regulation of coal mining machine and scraper conveyor. In the synthesized mining face, due to the uncertainty of the coal fall, the load of the scraper conveyor fluctuates due to the change of the coal load, which shows a strong nonlinearity and non-smoothness, leading to the difficulty of prediction. To solve this problem, this paper proposes a BP neural network model combined with wavelet transform for scraper conveyor current prediction. By studying the mapping relationship between motor load and current based on the BP neural network algorithm, and taking the scraper conveyor current as the input condition, wavelet decomposition and data reconstruction of historical current data are carried out, and time series prediction is performed on the original data samples and reconstructed data samples, respectively. The simulation results show that the reconstructed BP neural network model using wavelet decomposition has higher prediction accuracy, in which the root mean square error is reduced by 13.26%, the average absolute error is reduced by 14.19%, and the percentage error is reduced by 17.43%. The model meets the accuracy requirements of scraper conveyor load prediction, and can provide theoretical basis for cooperative speed regulation of coal mining machine and scraper conveyor.

The polynomial model and wood model have been extensively applied to predict the milk yield of cows, which aims to measure and partially explain the uncertainty under single factor condition. However, different sample data would affect the goodness of fit of the models. To investigate the milk yield regularities of the Chinese Holstein cows in Yunnan Standardized Pasture (YSP), data of cows with 1–3 parities using different observation periods were derived from the 401,497 records of 1,826 cows collected from YSP, and fitted in the BP neural network (BPNN) model, the Quadrinomial model and the Wood model. Prediction results of three models were compared. The results show that, for group data (mean), the Quadrinomial model was likely to over-fit under the single factor condition with small sample data, while the Wood model had a better goodness of fit; the BPNN model was more suitable for multi-factor and large sample data analysis.

Scientific and effective teaching quality evaluation (QE) is helpful to improve teaching mode and improve teaching quality. At present, calligraphy teaching (CT) QE methods are few in number and have poor evaluation effect. Aiming at these problems, deep learning (DL) is introduced to realize intelligent evaluation of CT quality. First, based on relevant research, the CTQE indicator system is constructed. Secondly, rough set and the principal component analysis (PCA) are used to reduce the dimension of the CTQE index system and extract four common factors. Then, the corresponding index data is input into the BP neural network (BPNN) model optimized by the improved sparrow search algorithm for fitting. Finally, combining the above contents, the improved sparrow search algorithm (ISSA) BPNN model is built to realize the intelligent evaluation of CT quality. The experimental results show that the loss value of ISSA-BPN model is 0.21, and the fitting degree of CT data is 0.953. The evaluation Accuracy is 95%, Precision is 0.945, Recall is 0.923, F1 is 0.942, and AUC is 0.967. These values are superior to the most advanced teaching QE model available. The SSA-BPNNCTQE model proposed in the study has excellent performance in CTQE. This is of positive significance to the improvement of teaching quality and students' calligraphy level.

The accurate calculation of the height of water-flowing fractured zone (WFFZ) in coal mine is a critical factor in ensuring mine safety and protecting surficial eco-environment. In view of inapplicability of traditional empirical formula for predicting the height of WFFZ, the correlation of the height of WFFZ and influence factors was analyzed firstly based on 82 collected groups of coalfield measured data in China. Results show that the mining thickness and mining depth have a significant effect on the height of WFFZ. Subsequently, the measured data were divided into two parts: 80% for training models and the remaining 20% for validation. Two prediction models, i.e., the multiple regression (MR) model and BP neural network (BPNN) model, were established and trained. A new merging model, multiple regression-BP neural network (MR-BPNN) model, was proposed by combining the multiple regression and BP neural network model. The prediction accuracy and generalization ability of the three models were verified by the recorded testing samples. Results of comparison suggest that three models all had better applicability for predicting the height of WFFZ in the coalmine, compared with the existing empirical prediction methods. More importantly, the MR-BPNN merging model combined the nonlinear mapping ability of neural network and empiric of multiple regression model, which could provide high-accurate, strong-generalized, and practical application for predicting the height of WFFZ of coalfield. In addition, the reason for the inapplicability of traditional empirical formula and the practicability of the proposed neural network-based prediction models were discussed.