Optimized Back Propagation Neural Network Research Articles

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

Optimizing reverse osmosis desalination from brackish waters: Predictive approach employing response surface methodology and artificial neural network models

A mathematical model frame has been developed to optimize the performance of reverse osmosis (RO) desalination in brackish waters with medium to high salt concentrations using response surface methodology (RSM) and artificial neural network (ANN), specifically catered towards efficiently operating RO desalination systems. The RSM model aims at predicting RO performance with second-order polynomials based on 15 experimental datasets. Inputs for RSM model include feed TDS concentration, feed water flow rate, and concentration-to-desalination ratio, while the outputs consider rejection, membrane flux, performance index, and specific energy consumption. The Genetic Algorithm optimized back-propagation Neural Network was employed to update network weights and biases, leading to an optimal ANN network structure of 3–10-4. A ratio of 70 % training, 15 % validation, and 15 % test data was chosen to establish an ANN model that predicts the performance of RO desalination. The ANN model demonstrated significantly higher prediction accuracy than the RSM model, with impressive correlation coefficients of 0.9936 for the performance index and 0.9932 for SEC prediction, and low RMSE of 0.85 for rejection. To maximize the performance index and minimize specific energy consumption, a two-step optimization method was further set up by applying the RSM model to initially determine optimal process conditions followed by the ANN model to enhance the accuracy of performance forecasts. Optimal process conditions were proposed for specific feed TDS scenarios, which involved setting a feed TDS concentration limit to meet drinking water standards.

FAGSO-BP neural network model for settlement prediction of soft soil foundation

To address the problem of difficulty in theoretical formulas and numerical analysis methods to consider the prediction of foundation settlement in the case of multiparameter coupling, the fireworks algorithm (FWA) with a gravity search operator (FAGSO) is introduced into the back propagation (BP) neural network model, and the weight and threshold processes in the neural network are improved by the FAGSO algorithm. A soft soil foundation settlement prediction model based on a FWA optimized BP neural network (FAGSO-BP) with a gravity search operator is proposed. Based on the second phase of the soft foundation treatment project of the Shatian Port Area, Humen City, and Dongguan City, the parameters of pile load height, pore water pressure, and groundwater level were collected and tested. The results indicate that the average relative error of the predicted cumulative settlement value of the soft soil foundation was 0.06%, root mean square error was 7.68, mean absolute error was 4.30, mean absolute percentage error was 1.40%, and mean square error was 59.98. Compared with the BP neural network, GA-BP neural network, and FWA-BP neural network, the error was smaller and the model stability was higher.

Research on the Prediction of Infiltration Depth of Xiashu Loess Slopes Based on Particle Swarm Optimized Back Propagation (PSO-BP) Neural Network

The Xiashu loess exhibits expansion when in contact with water and contraction when water is lost, making it highly susceptible to the influence of rainfall. Therefore, it is essential to investigate the infiltration behavior of rainwater in Xiashu loess slopes under various conditions. The depth of infiltration in slopes directly affects the depth of landslide failure and serves as an important indicator for studying slope infiltration characteristics; only a handful of academics have delved into its study. This article is based on on-site rainfall experiments on Xiashu loess slopes, using three main factors, rainfall intensity, rainfall duration, and slope angle, as discrimination indicators for the infiltration depth of Xiashu loess slopes. The particle swarm optimization algorithm is employed to optimize the BP neural network and establish a PSO-BP neural network prediction model. The experimental data are accurately predicted and compared with the multivariate nonlinear regression model and traditional BP neural network models. The results demonstrate that the PSO-BP neural network model exhibits a better fit and higher prediction accuracy than the other two models. This model provides a novel approach for rapidly determining the infiltration depth of Xiashu loess slopes under different rainfall conditions. The results of this study lay the foundation for the prediction of the landslide damage depth and infiltration of Xiashu loess slopes.

Intelligent optimization design of squeeze casting process parameters based on neural network and improved sparrow search algorithm

Squeeze casting process parameters are the key to squeeze casting production and to obtain excellent performance casts. To realize intelligent optimization design of process parameters under various requirements, this work presents a new intelligent optimization design framework for squeeze casting process parameters based on process data and integrating a two-stage intelligent integrated optimization. To adapt to diverse optimization applications of different process and target parameters, the backpropagation (BP) neural network and existing process data are utilized to intelligently establish the incompletely determined correlations between process parameters and squeeze cast quality or properties. An improved sparrow search algorithm (LCSSA) is developed and integrated to optimize both the aforementioned model structure and intelligently obtain the optimal solution, that is, the two stages of optimization. For the purpose of assessing the effect of each performance on the combination of process parameters, the information entropy weight approach is used. Various application cases and experiments have been conducted to evaluate the effectiveness of the suggested intelligent optimization design framework. It is indicated that the proposed method is practicable and can intelligently achieve ideal process parameters with high accuracy even based on small-scale data samples. The solving efficiency and optimization accuracy of LCSSA are superior than those of other intelligent optimization algorithms like the genetic algorithm (GA). The proposed framework outperforms the mixture of the BP neural network and other traditional optimization algorithms.

Intrusion detection method based on improved social network search algorithm

The network security problem in today's world is becoming more and more prominent, and intrusion detection as a branch in the field of network security has been developed tremendously. At present, back propagation (BP) neural network is widely used in intrusion detection. However, its weights and thresholds are randomly initialized, so that fall into local optimal after training. To solve this problem, an intrusion detection model based on improved social network search (ISNS) algorithm to optimize BP neural network is proposed. The model first uses chaotic mapping and elite mechanism to improve the original Social Network Search (SNS) algorithm, and then uses the ISNS algorithm to filter the initial weights and thresholds of the BP neural network, and constructs the neural network with the optimal values to avoid falling into local optimum. The experimental results show that the proposed method solves the problem that the traditional BP neural network is easy to fall into local optimum. At the same time, the ISNS_BP model achieves better classification performance than other models, and the accuracy on the NSL-KDD and UNSW-NB15 datasets reaches 98.62% and 93.97%, respectively.

Data-driven approaches for predicting performance degradation of solid oxide fuel cells system considering prolonged operation and shutdown accumulation effect

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized backpropagation neural network model for analysis. This model exhibits the best performance degradation evolution prediction compared to the long short-term memory and particle swarm optimization backpropagation neural network methods. This finding is important for monitoring and controlling the operational status of stack.

Particle Swarm Optimized Back Propagation Neural Network for State of Health Estimation of Lithium-ion Battery

The assessment and monitoring of battery health is very crucial for the maintenance and safety of battery-powered applications such as Electric vehicles (EVs). To conduct appropriate battery operation in EVs, the battery capacity should be estimated accurately. In this regard, the State of health (SOH) estimation is conducted for evaluating the battery aging status. This work proposes a hybrid backpropagation neural network (BPNN) and particle swarm optimization (PSO) technique for SOH estimation. A multi-feature input data framework is constructed with 31-dimensional features for the model training by using 4 battery datasets from NASA i.e. B5, B6, B7 and B18. The acquisition of the data samples has been performed with a systematic sampling technique. The presented work is conducted with a training testing ratio of 70:30 and validated with the MIT Stanford battery dataset. The experimental outcomes demonstrated high SOH estimation accuracy compared with the conventional BPNN model. In the case of battery B5, it was observed that RMSE, MSE and MAPE for the BPNN-PSO model are 0.6791, 0.0046, 0.3203 compared with the conventional BPNN model i.e. 0.8796, 0.0077, 0.4881 respectively. Furthermore, the significance of capacity regeneration in B7 and B18 results in high-performance metrics compared with other battery datasets. The research conducted would be beneficial to estimate the battery status regarding battery health i.e. SOH accurately in Battery System Management (BMS) based EV application.

Short-term photovoltaic output power prediction based on similar day and optimized BP neural network

Abstract The photovoltaic (PV) output power is affected by the ambient temperature, seasons, weather and other factors, which makes the PV output power very unstable. Therefore, accurate prediction of the PV output power is highly beneficial. This paper is dedicated to finding a simple and reliable PV short-term output power prediction method. First, we choose four key parameters, season, solar irradiance, temperature and relative humidity, to predict PV output power by using the similar day theory, which is mainly because the above four parameters are decisive for PV output power, although more parameters being taken into account will make the prediction accuracy higher, but it brings along with it an increase in the complexity; secondly, we choose the backpropagation (BP) neural network because it is very suitable for the PV output power prediction due to its excellent learning ability; finally, we optimize the standard BP neural network in loss functions, activation functions and optimizers to further improve its prediction accuracy. We validate the proposed method in different seasons and under other weather conditions. The results show that the proposed method has better prediction results, the optimized BP neural network has better performance compared with the standard BP neural network, and the standard deviation of the prediction is improved from ~1382, ~1571, ~1457, ~989 to ~903, ~792, ~333 and ~409.

Cotton Verticillium wilt monitoring based on UAV multispectral-visible multi-source feature fusion

Verticillium wilt seriously jeopardizes cotton growth and restricts cotton yields. Therefore, it is important to accurately, rapidly, and non-destructively estimate the extent of cotton Verticillium wilt (CVW). The focus of this study was to explore the potential of combining the vegetation index (VI), color index (CI), and texture features to improve the accuracy of CVW disease severity estimation based on hexacopter Unmanned Aerial Vehicle (UAV) images. Simple Linear Regression (LR) and Multiple Linear Regression (MLR) methods were used to determine correlations between VI, CI, texture, and normalized difference texture index (NDTI) variables and cotton Verticillium wilt disease index (DI). The LR model based on VI, CI, and NDTI was constructed, VIs, CIs, and NDTIs were fused, and Grey Wolf Optimizer (GWO) Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) Backpropagation Neural Network (BP) models were constructed to comparatively explore the estimation ability of each model on the degree of CVW disease. The results showed that VI was significantly correlated with DI, followed by NDTI, and CI. Compared with texture, NDTI containing both texture features was more significantly correlated with DI. The accuracy of the DI estimation using LR was highest for the one-factor VI model (R2 > 0.48, RRMSE < 51.48), followed by the NDTI model (R2 > 0.47, RRMS < 60.81) and the CI model (R2 > 0.33, RRMSE < 52.58). The PSO-BP and GWO-ELM were further used to model the DI estimation with different input variables. Regardless of the period, the fusion of three data sources (VIs + CIs + TIs) was preferable to a single data source or a combination of two data sources for different model inputs. In terms of different modeling algorithms, GWO-ELM combining VIs, CIs, and NDTIs had the highest estimation accuracy compared with SR and PSO-BP, with a validated R2 values of 0.65 (RRMSE = 42.96) at the flowering stage, 0.66 (RRMSE = 20.00) at the flower and boll stage, and 0.88 (RRMSE = 10.53) at the boll stage. This study demonstrated that the estimation accuracy of DI was significantly improved using collaborative modeling with multiple data sources. This study provides ideas and methods for monitoring crop disease conditions using low-altitude remote sensing.

Online monitoring system of material moisture content based on the Kalman filter fusion algorithm in air-impingement dryer

A Kalman filter fusion algorithm was proposed, and an online monitoring system was developed for real-time monitoring of the moisture content of materials in an air-impingement dryer. The Kalman filter algorithm was used to estimate the optimal state of the original detection values of the weighting sensor and air velocity sensor. A backpropagation (BP) neural network fusion model was established, where the weight detection value, elastic substrate temperature, air velocity, and impingement distance were considered inputs and the real weight of the material was the output. The optimal topology of the BP neural network was selected, and the initial weights and thresholds of the BP neural network were optimized using a genetic algorithm. The coefficient of determination (R2) and root mean square error (RMSE) of the optimized BP neural network fusion model were 0.9995 and 4.9, respectively. The Kalman filter fusion algorithm, which can realize online monitoring of moisture content, was established using the Kalman filter algorithm and fusion model. Moreover, an online monitoring system for material moisture content was developed, validation experiments were carried out, and the R2 and RMSE of the nine sets of validation experiments were 0.9963 and 0.78, respectively. The monitoring system satisfied the requirements of material moisture content detection accuracy in the drying process. The developed monitoring system is greatly important for improving the automation level of the drying equipment for fruits and vegetables. The proposed Kalman filter fusion algorithm also provides a reference for other multifactor fusion detection.

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China.

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Environmental Risk Assessment of the Harbin Section of the Songhua River Basin Based on Multi-Source Data Fusion

Surface water is a vital resource for human survival. However, economic and social development has resulted in significant pollutants from human activities, causing environmental pollution in watersheds. This pollution has had a profound impact on the surface water environment. However, limited studies have been conducted on the environmental risk evaluation of the watershed. In this study, we accounted for agricultural, industrial, and domestic source discharges in the districts and counties of the Harbin section of the Songhua River Basin for 2021. Data were collected from Statistical Yearbooks and governmental departments, and the characteristics of pollutant discharges in Harbin’s districts and counties were analyzed. Subsequently, we employed the Back Propagation neural network optimization method, combining remote sensing data, accounting data, pollution discharge data from each district and county, and economic and social data from the Statistical Yearbook and literature. This fusion of multiple data sources facilitated the construction of a watershed environmental risk evaluation system. The analysis considered four levels: economic and social, resource load, environmental infrastructure, and pollution discharge. Via this comprehensive evaluation, we identified the reasons for environmental risks in the water environment of the Harbin section of the Songhua River Basin. The evaluation results indicate that Nangang District, Xiangfang District, and Pingfang District face a higher risk to the water environment. Consequently, recommendations for mitigating water environment risks in these areas and across Harbin City are presented. The research methods and findings in this paper contribute valuable insights for developing control strategies to manage water quality in critically polluted areas of the Harbin section of the Songhua River Basin, providing a scientific foundation for regional river water quality management studies.

Optimizing Back-Propagation Neural Network to Retrieve Sea Surface Temperature Based on Improved Sparrow Search Algorithm

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network’s initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters.

Research on dynamic response prediction of semi-submersible wind turbine platform in real sea test model based on machine learning

The motion response of floating offshore wind turbine (FOWT) platform is closely related to its safety, and model test is the most direct method to fully study its dynamic characteristic behavior. In this paper the dynamic response of intermediate-scale physical semi-submersible FOWT model test under real sea environmental loads is studied and compared with the numerical model dynamic response, demonstrating that some limitations of small-scale physical model tests can be overcome, such as the catenary effect of mooring lines. It is found that the nonlinear dynamic characteristics of the physical model under actual marine environmental loads can be captured by the numerical model, which can improve the fidelity of model test data for semi-submersible FOWT structures in the existing literature. The dynamic response database of semi-submersible FOWT under complex wind, wave and current loads was established by using a numerical model with high accuracy, and was used in the prediction research of different machine learning algorithm models such as Genetic Algorithm optimization Back Propagation neural network (GA-BP), Support Vector Machine (SVM) and Gaussian Process (GP). The research shows that the relationship between the environmental load and the dynamic response of FOWT can be established by using the machine learning model, in which the number of samples has a significant impact on the prediction of the mooring line tension, and the research on the three algorithm models shows that GP performs significantly better than GA-BP and SVM in predicting the mean value of surge, pitch and mooring line force. For the dynamic response prediction of mooring line tension, GP model can get better results. In the future, this method will become an important method to predict and monitor the dynamic response of FOWT.

Flow Prediction of a Measurement and Control Gate Based on an Optimized Back Propagation Neural Network

The measurement and control gate, as a new type of measurement and control equipment, has been widely used for water quantity control in irrigation areas. However, there is a lack of methods for calibrating the flow inside the measurement box at present. This paper establishes a flow prediction model based on a back propagation (BP) neural network and its optimization algorithm by using 450 sets of sample data obtained from the indoor gate overflow test and verified the effectiveness and accuracy of the prediction model by using another 205 sets of sample data. The results show that the gate flow prediction model based on a BP neural network and its optimization algorithm has self-adaptability to different flow patterns, and its prediction accuracy is significantly higher than that of the traditional water measurement method. Compared to the unoptimized BP model, the BP model optimized by the genetic algorithm (GA) or particle swarm optimization (PSO) has higher prediction accuracy and better error distribution. Both GA and PSO algorithms can be used to optimize the initial weights and thresholds of the BP flow prediction model. However, by comprehensively analyzing the prediction accuracy, error distribution, and running time, the PSO algorithm has better optimization performance compared to the GA algorithm. The prediction model can provide a reference for flow rate calibration and the anomaly rejection of measurement and control gates in the irrigation area.

Optimized backpropagation neural network for risk prediction in corporate financial management

Corporate financial management is responsible for constructing, optimizing, and modifying finance-related structures for an unremitting function. The finance optimization model incorporates risk prediction and fund balancing for distinguishable corporate operations. This risk prediction is handled using sophisticated computing models with artificial intelligence and machine learning for self-training and external learning. Therefore, this article introduces a Backpropagation-aided Neural Network for designing an Optimal Risk Prediction (ORP-BNN) to pre-validate existing and new financial imbalances. The risk prediction model is designed to cope with corporate standards and minimum riskless financial management. This is designed as a linear snowfall model wherein the BNN decides the significance between fund allocation and restraining. The snowfall model significantly relies on allocation or restraining, which is achieved by assigning significant weights depending on the previous financial decision outcome. The weight factor is determined using gradient loss functions associated with the computing model. The training process is pursued using different structural modifications used for successful financial management in the past. In particular, the risk thwarted financial planning using a snowfall-like computing model, and its data inputs are used for training optimization. Therefore, the proposed model's successful risk mitigation stands high under prompt decisions.

Data-Based Shape Self-Sensing of a Cable-Driven Notched Continuum Mechanism Using Multidimensional Intrinsic Force Information for Surgical Robot

Abstract The accurate shape-sensing capability of the continuum mechanism is fundamental to improve and guarantee the motion control accuracy and safety of continuum surgical robots. This paper presents a data-based shape self-sensing method for a cable-driven notched continuum mechanism using its multidimensional intrinsic force information, which mainly includes the multidimensional forces/torques and driving cable tensions. The nonlinear hysteresis compensation and the shape estimation of the notched continuum mechanism play significant roles in its motion control. Calibration compensation of the notched continuum mechanism is performed based on kinematic modeling to improve the accuracy of its preliminary motion control. The hysteresis characteristics of the continuum mechanism are analyzed, modeled, and compensated through considering the abundant dynamic motion experiments, such that a feedforward hysteresis compensation controller is designed to improve the tracking control performance of the continuum mechanism. Based on the kinematic calibration and hysteresis compensation, combined with the motor displacement, driving cable tensions, and six-dimensional forces/torques information of the continuum mechanism, a data-based shape self-sensing method based on particle swarm optimization back propagation neural network (PSO-BPNN) is proposed in this study. Experimental results show that this method can effectively estimate the loaded and unloaded shape of the notched continuum mechanism, which provides a new approach for the shape reconstruction of cable-driven notched continuum surgical robots.

Intelligent Evaluation of Innovative Enterprise Performance - Construction of BPNN Model based on Improved WOA Optimization

With the economic progress, the environment in which enterprises operation is becoming increasingly complex. Intelligent performance evaluation of innovative enterprises is of great significance for their own development. The traditional performance evaluation indicators of enterprises rely too much on their financial indicators, leading producers and operators to pay more attention to the short-term financial performance growth of the enterprise. The long-term development of enterprises is neglected, resulting in weak core competitiveness. Therefore, to better achieve the scientific evaluation of innovative enterprise performance, based on the innovative enterprise performance evaluation index system, an innovative enterprise performance intelligent evaluation model with the whale optimization algorithm optimized backpropagation neural network is constructed. For the shortcomings of the whale optimization algorithm in the operation, the wolf swarm algorithm isintroduced to optimize it. The experimental results show that the evaluation model based on the improved whale optimization backpropagation neural network proposed in the study has very small errors in the evaluation results of different samples, with no more than 3%. This indicates that the performance evaluation index system for innovative enterprises can objectively reflect enterprise performance. This evaluation model can offer a reasonable analysis of enterprise performance, providing reference for intelligent evaluation of innovative enterprise performance.

Electricity demand forecasting based on feature extraction and optimized backpropagation neural network

As the global population is growing at a high rate, so is the electricity demand also increasing at a faster rate. This exerts pressure on electricity-generating plants and maintenance engineers because of the variability in demand. Avoiding disruption in the supply to meet demand requires forecasting what the future of demand will look like to be able to plan adequately towards it. This study, therefore, develops a new forecasting model using feature extraction (FE) where statistical information of the hourly demand data is extracted which serves as input variables for Backpropagation neural network (BPNN) optimized by particle swarm optimization (PSO) for electricity demand forecasting in Ghana. The model known as FE-PSO-BPNN is compared to other seven models such as Radial Basis Function (RBFNN), Random Forest (RF), Gradient Boosting Machine (GBM), Multivariate Adaptive Regression Splines (MARS), BPNN, and PSO-RBFNN where FE selects the input variables for all models. Electricity demand data from Ghana Grid Company from the period including 1st September 2018 to 30th November 2019 is used for the testing of the model's performance. Evaluation criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Scatter Index (SI) were used. The proposed model is more powerful in forecasting electricity demand than the others as it has RMSE (0.5344), MAE (3.3845), MAPE (0.1773), and SI (0.0003). The model is expected to be a better option for electricity sector managers when considering demand forecasting.