МОДЕЛИРОВАНИЕ НЕЙРОПРОГНОЗИРУЮЩЕГО УПРАВЛЕНИЯ ДОЖДЕВАЛЬНЫМИ МАШИНАМИ

An artificial neural network (ANN) representation method is presented and discussed in this paper. ANN representation of symbolic and numeric knowledge is realized by the improved backpropagation (BP) algorithm. The method of management and organization of ANN representation is presented also. The problem that numeric knowledge is difficult to be represented is effectively solved by the network. After learning, the threshold values and weights of ANN are ensured and the diagnostic knowledge is learned. The practical examples are used to check the diagnostic results. It is shown that the ANN representation method is effective.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system’s ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS’s efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management.

A method for modernizing the route of technical document flow at an enterprise manufacturing high-tech products proposed using elements of the theory of artificial neural networks. The traditional route of the processes of approval of technical documentation in the environment of an automated enterprise system revised. With analysis using elements of the theory of artificial neural networks, elements of the route that require improvement identified. An improved model of the technical document flow route in the environment of an enterprise automated system is proposed, which made it possible to improve the quality of the processes of approval of the enterprise's technical documentation.

This study presents a new model to predict the static formation temperature at multiple oil fields using multiple machine learning algorithms. Results are compared with the real temperatures obtained from two wells. The model developed in this study predicts static formation temperature according to several machine learning algorithms including artificial neural networks, fuzzy logic, k-nearest neighbors, and random forest algorithms. The following are the key findings: various geothermal wells in distinct fields or formations may have different machine learning connections. However, if a connection is defined using adequate field data, it is clear that static formation temperature can be approximated with great accuracy. Based on machine learning models we developed a novel model for forecasting static formation temperature, examined the modeling data and outcomes, and found that Random Forest, Fuzzy logic, and K-nearest neighbors outperformed Artificial Neural Network. Therefore, the performance of the proposed model for estimating static well temperature achieved a mean absolute percentage (AAPE) of 0.003%, and the coefficient of determination (R 2 ) is 0.99%. When the classical (mathematical) methods were compared to the artificial intelligence methods, the artificial intelligence methods produced more accurate results with varying percentages. The findings generated by the unique new model computation and the measured test data are substantially matched when compared to computed data and observed temperature data. The novelty of this newly developed AI model is that it will serve as a practical and inexpensive tool for SFT determination in geothermal and petroleum wells.

The objective of this research is to implement a method for estimating the real missing data in heart disease datasets and to show how it affects the resulting knowledge. Missing data is common problem in knowledge discovery from database (KDD) processes that can lead significant error in extracted knowledge. We use hybridization of artificial neural network and rough set theory (ANNRST) to estimate the real missing data on heart disease from UCI (University of California, Irvine) datasets. ANN with reduced input features is used to estimate the missing data. RST is used to reduce the dimensionality of input features and to extract the knowledge as reducts and rules from heart disease datasets with estimated missing data. RST, decomposition tree, local transfer function classifier (LTF-C) and k-nearest neighbor (k-NN) classifier are used to calculate the accuracy. Comparative study with k-NN estimation, most common attribute value filling and deletion of missing data are made to evaluate the extracted knowledge. ANNRST can be considered as the appropriate estimation method when strong relationship between original complete datasets and estimated datasets is important (the estimated datasets really represent the nature of original complete datasets) as it gives the best accuracy and coverage for almost all the classifiers.

In this work we sketch a new theory of artificial neural network, based on a paraconsistent annotated logic Eτ. Such theory, called Paraconsistent Artificial Neural Network – PANN – is built from the algorithm Para-analyzer and has as characteristics the capability of mainly manipulating uncertainty, inconsistent and paracomplete concepts. Some aspects such as capability of adaptation, velocity processing, and other useful characteristics make the PANN a promising theory.

Introduces the theory of artificial neural networks (ANN). Discusses its application to the valuation of residential apartments. Compares the performance of the back propagation neural network (BP) model in estimating sale prices of apartments against the traditional multiple regression analysis (MRA) model. Concludes that the neural network model is an easy‐to‐use, black‐box alternative to the MRA model.

Laser powder bed fusion (L-PBF) parts often require post-processing prior to use in engineering applications to improve mechanical properties and modify the as-built surface topography. The ability to tune the L-PBF process parameters to obtain designer as-built surface topography could reduce the need for post-processing. However, the relationship between the as-built surface topography and the L-PBF process parameters is currently not well-understood. In this paper, we derive data-driven models from surface topography data and L-PBF process parameters using machine learning (ML) algorithms. The prediction accuracy of the data-driven models derived from ML algorithms exceeds that of the multivariate regression benchmark because the latter does not always capture the complex relationship between the as-built surface topography parameters and the corresponding L-PBF process parameters in a single best-fit equation. Data-driven models based on decision tree (interpretable) and artificial neural network (non-interpretable) algorithms display the highest prediction accuracy. We also show experimental evidence that thermocapillary convection and melt track overlap are important drivers of the formation of as-built surface topography.

Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.

The aim of this work is to develop an intelligent method for controlling the induction soldering process of spacecraft’s thin-walled aluminum waveguide tracks. This new control method is based on the use of intelligent data analysis in order to determine the optimal control algorithms for product heating and the workpiece motion control with optimal coefficients based on the operational data of the induction soldering technological process. The paper presents a mathematical formulation of the intelligent control problem. As part of this work, such intellectual methods have been chosen for comparison as a method based on a fuzzy logic, artificial neural networks, and a neuro-fuzzy controller. Based on experimental data on real technological processes of induction soldering, a comparative analysis of the effectiveness of the proposed approach has been carried out. The results of experimental studies have shown that the control method of the induction soldering technological process based on artificial neural networks has the highest efficiency. In addition, based on experimental data of real technological processes, the most effective structure of an artificial neural network has been determined. The highest recognition accuracy has been provided by an artificial neural network with five artificial neurons on each one of five hidden layers. Experimental quality control of the obtained artificial neural network has been carried out on three different waveguide tracks assemblies with different tube thicknesses: 58×25 mm, 35×15 mm, 19×9.5 mm. The results of the experimental verification have showed that for all the three waveguide tracks assemblies high-quality soldered joints have been obtained. The integrated application of intelligent technologies for controlling the technological process of induction soldering of thin-walled aluminum waveguide tracks could significantly improve the quality of permanently formed connections, as well as eliminate errors associated with human factor. Further research will focus on the intellectualization of other technological processes for creating permanent connections, such as electron beam welding, diffusion welding, etc. The results of this work clearly show the effectiveness of the proposed concept of intellectualization, so it is advisable to extend it to a wider range of technological processes.

Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects

An integrated Data Envelopment Analysis–Artificial Neural Network–Rough Set Algorithm for assessment of personnel efficiency

The diagnosis of incipient fault is important for power transformer condition monitoring. Incipient faults are monitored by conventional and artificial intelligence based models. Key gases, percentage value of gases, and ratio of the Doernenburg, Roger, IEC methods are input variables to artificial intelligence models, which affects the accuracy of incipient fault diagnosis, so selection of the most influencing relevant input variable is an important research area. With this main objective, Waikato Environment for Knowledge Analysis software is applied to 360 simulated samples having different operating lives to find the most influencing input parameters for incipient fault diagnosis in the gene expression programming model. The Waikato Environment for Knowledge Analysis identifies%C2H2,%C2H4,%CH4, C2H6/C2H2, C2H2/C2H4, CH4/H2, C2H4/C2H6, and C2H2/CH4 as the most relevant input variables in incipient fault diagnosis, and it is used for fault diagnosis using different artificial intelligence methods, i.e., artificial neural networks, fuzzy logic, support vector machines, and gene expression programming. The compared results shows that gene expression programming gives better results than the artificial neural network, fuzzy logic, and support vector machine with accuracy variation from 98.15 to 100%, proving the gene expression programming method can be used in transformer fault diagnosis research.