Research on Driving Cycle Recognition Strategy Based on Machine Learning

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

The aim of this work was to develop an accurate model to swiftly forecast the ultimate conditions and the stress–strain curves of ultra-high-performance concrete (UHPC) confined by fibre-reinforced polymer (FRP). Four neural network predictive models based on machine learning (back-propagation neural network (BPNN), support vector regression (SVR), extreme learning machine (ELM) and generalised regression neural network (GRNN)) were constructed to forecast the ultimate conditions. A BPNN-based model to predict stress–strain curves was developed. A database of 193 compression test results of FRP-confined UHPC members was collected from the open literature and a thorough evaluation of data quality was conducted. The results showed that the SVR, BPNN, GRNN and ELM prediction models exhibited excellent accuracy when compared with a design-oriented model. The GRNN exhibited the highest predictive precision, followed by SVR. The developed prediction models establish a foundation for the design and quick prediction of FRP-confined UHPC.

Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.

Surrogate-based multiobjective optimization to rapidly size low impact development practices for outflow capture

In order to make the plug-in hybrid electric vehicle obtain optimal energy consumption economy and adapt to more complex working environment, the optimization energy management strategies based on driving cycle recognition were made.First, six kinds of cycle as standard working were selected to represent urban congestion, city suburban and highway, and the characteristic parameters of block segmentation were calculated by use of composite uniform method.Second, the extreme learning machine was applied to train and identity working conditions.Third, the optimum algorithm was applied to calculate the energy distribution rules of six standard cycles, which was stored control parameter library in order to call.On the MATLAB/SIMULINK platform, the optimization mode was built and the energy management strategy of conditions recognition and conditions without recognition were simulated.Simulation results indicate that the energy consumption economy of control strategy based on driving cycle recognition have improved 13.8%,16.4%,14.8%, 11.1%,when the initial value of SOC is 0.95,0.75,0.55 and 0.35.

Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

Air overpressure (AOp) is a hazardous effect induced by the blasting method in surface mines. Therefore, it needs to be predicted to reduce the potential risk of damage. The aim of this study is to offer an efficient method to predict AOp using a cascaded forward neural network (CFNN) trained by Levenberg–Marquardt (LM) algorithm, called the CFNN-LM model. Additionally, a generalized regression neural network (GRNN) and extreme learning machine (ELM) were employed to demonstrate the accuracy level of the proposed CFNN-LM model. To conduct the CFNN-LM, GRNN, and ELM models, an extensive database, related to four quarry sites in Malaysia, was used including 62 sets of dependent and independent parameters. Next, the performances of the aforementioned models were checked and discussed through statistical criteria and efficient graphical tools. Finally, the results showed the superiority of CFNN-LM (R2 = 0.9263 and RMSE = 3.0444) over GRNN (R2 = 0.7787 and RMSE = 5.1211) and ELM (R2 = 0.6984 and RMSE = 6.2537) models in terms of prediction accuracy. Furthermore, three different regression analysis metrics were used to perform the sensitivity analysis, and according to the obtained results, the maximum charge per delay ( $$\beta$$ = 0.475, SE = 0.115, t-test = 4.125) was considered as the most influential feature in modeling the AOp.

Water consumption of plants is a key parameter for formulating irrigation system, and the precise prediction play a important role in improving the use efficiency of limited water resources. In this experiment, by using the method of artificial neural network and MATLAB DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed, soil water content and dew point temperature as the input variable, the author established the artificial neural network system to forecast the seedling water consumption of P.×euramericana cv."74/76", and through the experiments it has been examined that two neural network system models can be applied in forecasting water consumption of seedlings, and the average relative error of Back Propagation (BP) neural network prediction model was 0.07, the General Regression Neural Network (GRNN) prediction model was 0.05, moreover, the latter had good stability, while that of the former was poor. Therefore, we propose that GRNN model can be used in prediction of seedling water consumption. Furthermore, the maximum relative error of GRNN predication model was 0.106, the minimum relative error was 0.015. The GRNN model is superior to the BP neural network model that the former performs a higher forecasting accuracy with relatively shorter time consumption and faster speed in training.

Simple introduces the structure of generalized regression neural network, describes the process that using generalized regression neural network (GRNN) modeling to RF nonlinear system, and through the simulation results demonstrate this new nonlinear modeling method of RF devices. The simulation results of the nonlinear RF devices by General regression neural network (GRNN) control the relative forecasting error within in 0.05 The best predict relative error by BP neural network is 0.17, so, compared with BP neural network, GRNN neural network effectively improved the accuracy and precision of the model. In the simulation process by general regression neural network (GRNN) method, to the nonlinear RF power amplifier device, the prediction curve before data normalized more close to the set output curve.

To quickly and accurately assess tea plant growth, this study aims to find a new way to predict the chlorophyll content in tea plant canopies using machine learning. Using remotely piloted aircraft equipped with multispectral cameras, images of tea plantation areas are captured and reflectance from four spectral bands is extracted, leading to the calculation of vegetation indices. Simultaneously, chlorophyll relative content in the tea plant canopies was collected on the ground using a detector. Four models, namely Random Forest (RF), Backpropagation neural network (BPNN), Radial basis function network (RBFN), and General Regression Neural Network (GRNN), were constructed to predict the chlorophyll relative content in tea plant canopies. Subsequently, important remote sensing variables were identified through RF filtering, followed by a comparison of the predictive performance of machine learning models under different input conditions. Lastly, by integrating the Sparrow Search Algorithm (SSA) to optimize the smoothing factor in the GRNN, the study explores the impact of optimization algorithms on the predictive performance of the GRNN model. Experiments indicate that within the established machine learning models, the GRNN demonstrates the highest predictive accuracy. By ranking the importance of remote sensing variables through RF, 18 significant remote sensing variables were selected, which enhanced the predictive accuracy of the machine learning models. The optimization of the GRNN smoothing factor through the SSA algorithm can significantly enhance the predictive accuracy of the GRNN model. Based on a series of experiments, the established RFSSA-GRNN prediction model demonstrates good predictive performance, with an reaching 0.84.

Application of small sample virtual expansion and spherical mapping model in wind turbine fault diagnosis

We previously used back propagation neural network (BPNN) with the meteorological factors of the receiver point to establish a model for predicting propagation delay of Loran-C signal over a short distance. Nevertheless, for a long propagation path, it is not proper to use only the meteorological factors of the receiver point. In this letter, a propagation delay prediction model that considers multiweather and multipoint was established by using a more suitable generalized regression neural network (GRNN) over a long distance. We first compared three meteorological factors of six points on the propagation path, and found that they have obvious differences. Then, a propagation delay prediction model based on three meteorological factors of the six points is established with GRNN. Finally, the further comparison shows that on the one hand, GRNN is more suitable to establish the prediction model of propagation delay than BPNN; on the other hand, more points on the propagation path are considered, and the prediction accuracy of the model is higher.

Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China. However, overloaded vehicles have been causing frequent traffic accidents. Thus, to alleviate or resolve the corresponding problems associated with highway engineering safety and the market economy, an improved technique for overload management is urgently required. In this study, to analyze the overload data on expressways and highways in China, we developed a machine learning model by comparing the performances of cluster analysis, backpropagation neural network (BPNN), generalized regression neural network (GRNN), and wavelet neural network (WNN) in analyzing global and local time series overload data. In a case study, our results revealed the trends of overloading on highways in Jiangsu Province. Given sufficient data, BPNN performed better than GRNN and WNN. As the amount of training data increased, GRNN performed better, but the runtime increased. WNN had the shortest runtime among the three methods and could reflect the future trends of the overload rate in the monthly data prediction of overload. Our model provides information with potential value for expressway network management departments through data mining. This information could help management departments allocate resources reasonably and optimize the information utilization rate.

At present, there are mainly three research ways on the distribution of snow load: field measurement, experimental simulation, and numerical simulation. They all need expensive equipment, specimen model used only once, and slight defective simulation software. Artificial Neural Networks (ANNs) rely on a large number of input functions to estimate unknown or known occurrences. This paper applies ANNs to predict the uneven distribution of snow loads. First, the snow load database is collected and set up, including all the data from field measurement and experimental simulation. Second, part of the data from the 1 m3 model is used as a training set to train Backpropagation Neural Networks (BPNN) and Generalized Regression Neural Networks (GRNN). The parameters of the two neural networks are determined (such as the smoothing factor in the GRNN), and the rest of the data is used to examine the model; the prediction and measurement results of the two models are compared. Finally, based on different wind direction angles and wind speed, a GRNN model is established to predict the uneven snow load distribution. Through comparative analysis, GRNN is more suitable for predicting the uneven distribution of snow loads, especially in predicting snow thickness. In particular, the prediction effect of the model on the snow load distribution trend is good, and the prediction ability of the neural network model is displayed.