Neural Network Particle Swarm Research Articles

Adaptive velocity control for UAV boat landing: A neural network and particle swarm optimization approach

Achieving precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms, such as Autonomous Surface Vehicles (ASVs), is challenging, particularly in GPS-denied environments with dynamic disturbances. Conventional methods often rely on high-level waypoint navigation, extensive manual tuning, and expensive sensors. In this work, we propose an adaptive Proportional-Integral-Derivative (PID) controller optimization using a Neural Network-Particle Swarm Optimization (NN-PSO) algorithm. The algorithm dynamically tunes the PID controller, significantly reducing manual tuning effort, while relying solely on a low-cost camera and altitude sensor. The NN-PSO algorithm allows the UAV to land with an average error of 5 cm on static platforms and 10 cm on moving boats, based on multiple test flights. Our method also increases the maximum landing speed to 80.9% of the UAV’s top flight speed, a considerable improvement over existing systems. Our approach not only optimizes landing precision but also introduces techniques for ensuring soft landings, reducing oscillations, and preventing target misses. These enhancements make the method robust across varying flight altitudes and ASV speeds. Furthermore, this approach is applicable to a variety of GPS-denied scenarios, including rescue missions, package deliveries, and workspace inspections, without requiring costly equipment or extensive parameter tuning. Field experiments confirm the precision and stability of the proposed system, validating its performance in real-world conditions. [Video]

Prediction of Sunspot Activity Based on Differential Evolution Algorithm and BP Neural Network Model

In this research report, the prediction method of sunspot activity is deeply discussed, and a variety of statistical and data science models are used to make comprehensive prediction. Research focuses include the start time and duration of the solar maximum in the solar cycle, as well as the prediction of the number and area of sunspots. By capturing the periodicity of solar activity, Pearson correlation coefficient is used to analyze the relationship between the maximum solar activity and the number of sunspots, and the adaptive multivariate nonlinear regression-BP neural network model and particle swarm optimization BP neural network are combined to make high-precision prediction. The research results provide a scientific basis for the prediction of space weather, ionospheric state and communication system reliability.

Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils

The Kovats retention index is a critical parameter in gas chromatography used for the identification of volatile compounds in essential oils. Traditional methods for determining the Kovats retention index are often labor-intensive, time-consuming, and prone to inaccuracies due to variations in experimental conditions. This study presents a novel approach combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) to predict the Kovats retention index of essential oil compounds more accurately and efficiently. The ANN-PSO hybrid model leverages the strengths of both techniques: the ANN's capacity to model complex nonlinear relationships and PSO's capability to optimize hyperparameters by finding the global optimum. The model was trained using a dataset of 340 essential oil compounds with molecular descriptors, with the performance evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results indicate that a simpler ANN configuration with one hidden neuron achieved the lowest RMSE (80.16) and MAPE (5.65%), suggesting that the relationship between the molecular descriptors and the Kovats retention index is not overly complex. This study demonstrates that the ANN-PSO model can serve as an effective tool for predictive modeling of the Kovats retention index, reducing the need for experimental procedures and improving analytical efficiency in essential oil research.

Enhancing the reliability of CSP solder joints under thermal cycling conditions through particle swarm optimization of an improved BP neural network

The packaging of chip-scale (CSP) devices plays a pivotal role in enhancing the reliability of CSP under thermal cycling conditions, largely due to the intricate structure and the recurrent alterations in the actual operating environment. The objective of this study is to enhance the reliability of solder joints by optimising the structural parameters of CSP packaging in order to reduce the strain values at the solder joints. In order to enhance the design efficiency and accuracy of the computational model, the Anand model is adopted in order to define the solder joint parameters, and finite element simulations are conducted using Ansys software. This paper puts forward a novel intelligent algorithm that fuses response surface methodology with a neural network-particle swarm optimization algorithm, thereby enhancing the precision of the system. The method is capable of identifying the combination with the minimum strain value using limited data, thereby addressing the issue of insufficient generalisation ability in conventional methods. The implementation of this method into the model resulted in a minimum strain value of 4.071 × 10−3, representing a 37.6 % reduction compared to the original CSP structure. Furthermore, the optimized lifespan is approximately 3.24 times longer than that observed prior to optimization. The approach presented in this paper has the potential to significantly enhance design efficiency and increase the lifespan of components. It offers a novel perspective for optimising the structural parameters of CSP packaging.

Heat transfer performance optimization of turbine blade internal cooling channel with pin-fins and recessed-extruded endwalls

This paper involves implementing and optimizing a novel recessed-extruded endwall design for a rectangular cooling channel equipped with pin-fins. The study encompasses an in-depth exploration of the flow dynamics, pondering the generation and sustenance of vortices within the channel, using Reynolds-averaged Navier-Stokes analysis. The study delves into the evaluation of heat transfer characteristics, considering crucial parameters such as Nusselt number, friction factor, and overall heat transfer performance. These attributes are contrasted with the corresponding values observed in cases featuring flat endwalls, concerning Reynolds numbers spanning from 7400 to 36,000. A parametric study of the channel with recessed-extruded endwalls is conducted, aiming to comprehend the influence of geometric factors on channel heat transfer. A single-objective optimization process focusing on enhancing heat transfer effectiveness, quantified through the heat transfer efficiency index (HTEI), is conducted with three design variables. The results indicate that the new endwall configuration significantly enlarges the high heat transfer areas near the pin-fins compared to the flat endwall across all Reynolds numbers. This novel endwall design substantially elevates the heat transfer levels adjacent to the pin-fins, showcasing an impressive 62.4% increase in HTEI at Re = 21,500 compared to the flat endwall, with a protrusion height of 2.5% of the channel height. Furthermore, by increasing the height, the recessed-extruded endwall configuration can achieve a remarkable 91.1% increase in HTEI compared to the flat endwall. This achievement is reached with a protrusion height equivalent to 5% of the channel height at Re = 7400. Through design optimization using the radial basis neural network (RBNN) surrogate model and particle swarm optimization (PSO) technique, the HTEI of the recessed-extruded endwall design shows enhancements of 91.7% and 18.0% compared with the flat endwall and reference designs, respectively, at a Reynolds number of 21,500. The present work demonstrates the potential for enhancing the heat transfer of pin-fins channels by improving endwall design.

Modeling of scour hole characteristics under turbulent wall jets using machine learning

The novelty of the present study is to investigate the parameters that depict the scour hole characteristics caused by turbulent wall jets and develop new mathematical relationships for them. Four significant parameters i.e., depth of scouring, location of scour depth, height of the dune and location of dune crest are identified to represent a complete phenomenon of scour hole formation. From the gamma test, densimetric Froude number, apron length, tailwater level, and median sediment size are found to be the key parameters that affect these four dependent parameters. Utilizing the previous data sets, Multi Regression Analysis (linear and non-linear) has been performed to establish the relationships between the dependent parameters and influencing independent parameters. Further, artificial neural network-particle swarm optimisation (ANN-PSO) and gene expression programming (GEP) based models are developed using the available data. In addition, results obtained from these models are compared with proposed regression equations and the best models are identified employing statistical performance parameters. The performance of the ANN-PSO model (RMSE = 1.512, R2 = 0.605), (RMSE = 6.644, R2 = 0.681), (RMSE = 6.386, R2 = 0.727) and (RMSE = 1.754, R2 = 0.636) for predicting four significant parameters are more satisfactory than that of regression and other soft computing techniques. Overall, by analysing all the statistical parameters, uncertainty analysis and reliability index, ANN-PSO model shows good accuracy and predicts well as compared to other presented models.

Interpretable multi-task neural network modeling and particle swarm optimization of process parameters in laser welding

The process parameters directly affect the quality and performance of laser welding (LW) by shaping the molten pool appearance. However, with the increase in the dimensions of process parameters and the number of samples, the relationship between process parameters and molten pool often exhibits high levels of uncertainty and nonlinearity. Machine learning (ML) techniques have been used for complex LW systems modeling, but they may be limited and unable to fully capture the complex relationships when faced with complex systems. Additionally, traditional ML techniques may lack interpretability within the model, making it difficult to understand the input features and their impact on the output. To address these issues, a method that combines an interpretable multi-task neural network (MTNN) and particle swarm optimization (PSO) is proposed. Firstly, an LW experimental platform is constructed, and the orthogonal design is conducted for data collection. In the data preparation stage, the curriculum learning strategy is employed to reorganize the raw experimental data. Subsequently, the MTNN is constructed to automatically learn feature representations among input process parameters, capture nonlinear relationships between data, and share weights across multiple related tasks to improve efficiency and performance. Furthermore, the PSO is designed to determine the constraints and objective functions. In addition, the shapley additive explanation (SHAP) method is utilized to calculate the importance of process parameters and enhance the interpretability. Finally, the optimization results are validated, and the results demonstrate that the optimized results of the proposed method have achieved satisfactory performance compared to the traditional ML methods.

Towards accuracy improvement in solution of inverse kinematic problem in redundant robot: A comparative analysis

AbstractThe redundant manipulators have more DOFs (degree of freedoms) than it requires to perform specified task. The inverse kinematic (IK) of such robots are complex and high nonlinear with multiple solutions and singularities. As such, modern Artificial Intelligence (AI) techniques have been used to address these problems. This study proposed two AI techniques based on Neural Network Genetic Algorithm (NNGA) and Particle Swarm Optimization (PSO) algorithm to solve the inverse kinematics (IK) problem of 3DOF redundant robot arm. Firstly, the forward kinematics for 3 DOF redundant manipulator has been established. Secondly, the proposed schemes based on NNGA and PSO algorithm have been presented and discussed for solving the inverse kinematics of the suggested robot. Thirdly, numerical simulations have been implemented to verify the effectiveness of the proposed methods. Three scenarios based on triangle, circular, and sine-wave trajectories have been used to evaluate the performances of the proposed techniques in terms of accuracy measure. A comparison study in performance has been conducted and the simulated results showed that the PSO algorithm gives 7% improvement compared to NNGA technique for triangle trajectory, while 2% improvement has been achieved by the PSO algorithm for circular and sine-wave trajectories.

Temperature compensation model for non-dispersive infrared CO2 gas sensor based on WOA-BP algorithm

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor.

Incident angle optimization with multiple conflicting objectives based on BP-MOPSO in oblique laser interferometry

The oblique incident phase-shifting interferometric measurement method is an effective technique for assessing gear tooth form deviation. In the measurement process, the tooth flank incident angle is a crucial factor determining the quality of interferograms. Addressing the challenge in traditional optical path design, where the selection of the tooth flank incident angle encounters a trade-off among quality features such as measurable tooth area (MTA), interference fringe density (IFD) and interferogram compression ratio (ICR), this paper proposes an optimized method for the tooth flank incident angle. Firstly, a comprehensive evaluation model is established to calculate and analyze the MTA, IFD and ICR of the helical gear. Secondly, by integrating BP neural network and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, a tooth flank incident angle optimization method is devised to simultaneously optimize the measurement light incident angle γ and the rotation angle of the gear axis β. Finally, simulation analyses and physical experiments are employed to demonstrate the feasibility of the proposed method, compared to before optimization, the pseudocorrelation (PSD) value has increased by an average of 34%, and the residual points have decreased by an average of 80%, confirming its capability to improve quality interferogram and measurement accuracy.

A Hybrid Neural Network‐Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

AbstractIn gas‐fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K‐Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators.

Optimization of the double-slot blown airfoil with jet at the leading and trailing edges of the flap

An active lift augmentation technique combining trailing edge blowing and blown flap is explored in this study. A double-slot blown flap airfoil is designed based on NACA23015. By optimizing the configuration parameters via the neural network surrogate model and particle swarm optimization algorithm, the lift coefficient at the 8° angle of attack reaches ∼5.14, which is approximately a 24% increasement relative to the baseline single-slot blown flap airfoil. In the configuration optimization, a longer flap facilitates lower pressure on the upper airfoil surface. Additionally, the aerodynamic flap effect caused by the double-slot jet flow intensifies the lift peaks and the pressure decreases over the airfoil. Ultimately, redistributing the jet momentum to the two slots achieves a highly efficient blown flap airfoil design.

Multi-objective optimization design of aerodynamic and infrared characteristics of multi-stream serpentine nozzle

A multi-objective optimization design of multi-stream serpentine nozzle was carried out to improve the aerodynamic performance and reduce the infrared radiation signal. A numerical simulation was carried out to investigate the effects of nozzle pressure ratio (NPR), Aspect ratio (AR) and Ratio of length and diameter (L/D). Computational fluid dynamics (CFD) simulation was employed to study the aerodynamic performance of the multi-stream serpentine nozzle, and the narrow-band method with discrete transfer method were used to calculate its infrared radiation intensity distribution characteristics. A 3-factor, 21-level orthogonal table was designed based on the orthogonal experimental method, and multi-stream serpentine nozzle with different geometrical parameter and operating conditions were designed to obtain the aerodynamic performance and infrared radiation intensity data of each experimental sample point. Radial Basis Function (RBF) neural network and Multi-Objective Particle Swarm Optimization algorithm were used to optimize the geometry and operating conditions with better aerodynamic performance and weaker infrared signal. The result shows that the Pareto solutions obtained by the optimization have good accuracy compared with the numerical simulation results, with an aerodynamic performance error of 0.02% and an infrared radiation intensity error of 0.24%.

Enhancing Thermo-Acoustic Waste Heat Recovery through Machine Learning: A Comparative Analysis of Artificial Neural Network–Particle Swarm Optimization, Adaptive Neuro Fuzzy Inference System, and Artificial Neural Network Models

Waste heat recovery stands out as a promising technique for tackling both energy shortages and environmental pollution. Currently, this valuable resource, generated through processes like fuel combustion or chemical reactions, is often dissipated into the environment, despite its potential to significantly contribute to the economy. To harness this untapped potential, a traveling-wave thermo-acoustic generator has been designed and subjected to comprehensive experimental analysis. Fifty-two data corresponding to different working conditions of the system were extracted to build ANN, ANFIS, and ANN-PSO models. Evaluation of performance metrics reveals that the ANN-PSO model demonstrates the highest predictive accuracy (R2=0.9959), particularly in relation to output voltage. This research demonstrates the potential of machine learning techniques for the analysis of thermo-acoustic systems. In doing so, it is possible to obtain an insight into nonlinearities inherent to thermo-acoustic systems. This advancement empowers researchers to forecast the performance characteristics of alternative configurations with a heightened level of precision.

Heat transfer enhancement in multijet micropin fin heat sink

The thermal performance of tiny heat sinks with liquid cooling is being studied in order to solve the issue of electronics cooling. Increasing thermal performance while lowering pressure drop and maintaining a constant substrate temperature is a difficult task. The purpose of this study is to harvest the useful features of combination of microjet and micropin fins and explore the influence of geometrical features on fluid structures and heat transfer. To comprehend and assess the performance of a multijet heat sink with a micropin fin, three-dimensional numerical simulation is used in this inquiry. The three parameters under consideration are, the ratio of the jet diameter to the pin fin diameter (α), which ranges from 1.5 to 6.0; the ratio of the jet diameter to the jet standoff distance (β), which ranges from 0.75 to 1.5; and the pin fin pitch is determined by the ratio of the pin fin diameter to the pitch length (λ), which ranges from 0.1 to 0.8. The grid independence test is done to ensure accuracy in numerical results. The numerical scheme is validated by comparing numerical results with that of experimental results from literature. The results obtained from numerical simulation are analyzed. It has been found that improving heat transfer is not just caused by expanding the contact surface area; fluid structure vortex generation also affects overall thermal performance. Better overall performance is produced at lower values of α, β and higher values of λ. The ideal design parameters are α = 1.99, β = 1.431, and λ = 0.699, according to the optimization of geometrical parameters using a radial basis neural network surrogate model and particle swarm optimization. The jet Reynolds number is found to have no significant impact on the overall performance.

Parameter calibration method of clustered-particle logic concrete DEM model using BP neural network-particle swarm optimisation algorithm (BP-PSO) inversion method

A high precision back propagation neural network-particle swarm optimization (BP-PSO) algorithm inversion method is proposed to calibrate the microparameters of the clustered-particle logic concrete discrete element method model. The calibration targets include initial damage, failure mode, and mechanical properties of the material. The research utilise 243 training datasets generated through orthogonal experimental design and conducted simulations to train the BP neural network. In addition, a parameter sensitivity analysis is employed on the trained BP neural network to quantify the impact level of each microparameter and guide future macroparameter fine-tuning. The results indicate that the mean absolute percentage error of the BP-PSO inversion method is only 3.79%. This research also study on the concrete failure mechanism by using a mesoscale model based on clustered-particle logic, which considered concrete as a three-phase composite composed of mortar matrix, aggregates, and interfacial transition zone. The crack initiation, propagation and coalescence of DEM model show a good agreement with the experimental results.

The Planning & Designing of Urban Building Water Supply and Drainage Engineering in AI Era

Abstract To explore how to improve the quality of urban building water supply and drainage engineering and further promote the economic construction of urban buildings, this paper combines artificial intelligence with water supply and drainage engineering of urban buildings. A detailed analysis of water supply and drainage engineering is unfolded, and the combination of BP neural network algorithm and particle swarm algorithm is used to improve the convergence accuracy of the algorithm and network generalization ability to optimize the BP neural network algorithm and construct the artificial intelligence algorithm model. Through experiments in the artificial intelligence algorithm model, the actual output quality parameter is 82, and the difference with the desired output quality parameter of 81 is 1, while in the ordinary design, the actual output quality parameter is 70, and the difference with the desired output quality parameter is 11, get in the artificial intelligence model the desired output and the expected output close. It shows that combining artificial intelligence with urban building water supply and drainage engineering can improve the quality of the project, which is more accurate and superior to the ordinary design and reduce the project cost.

An integrated modelling framework for multiple pollution source identification in surface water

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as “Excellent prediction”. The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

MOPSO-based structure optimization on RPV sealing performance with machine learning method

The structure integrity of reactor pressure vessels (RPV) plays an important role in the safety of nuclear power plants, which mainly concerns the risk of sealing failure. In the present work, a hybrid method consisting of an artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) is proposed, aiming to find an optimized RPV structure with improved sealing performance. The ANN model provides a more accurate prediction of sealing performance than the radial basis function neural network (RBFNN) model. There is also a better generalization ability in the ANN model with R2 above 0.96 and RMSE below 0.06 in the test set. With ANN as a surrogate model, multi-objective optimization is applied in RPV structure design. Compared with that of the original RPV structure, the sealing performance can be improved by even over 25% in the optimized structure. The application of ANN surrogate models can save more than 99% computation cost, compared with optimization based on finite element analysis (FEA).

Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction

This study uses advanced machine learning approaches to predict the kerf open deviation (KOD) when a CO2 laser is used to cut polymeric materials. Four polymeric materials, namely polyethylene (PE), polymethyl methacrylate (PMMA), polypropylene (PP), and polyvinyl chloride (PVC), were cut under the same conditions. The process control factors were the power of the laser beam (80–140 W) and cutting speed (1–6 mm/s), while sheet thickness, standoff distance, and gas pressure were kept constant during experiments. KOD between the upper and lower opens of the kerf was the process response. KOD was predicted using three machine learning models, namely a conventional artificial neural network (ANN), a hybrid neural network–humpback whale optimizer (HWO-ANN), and a hybrid neural network–particle swarm optimizer (PSO-ANN). Experimental data for all polymeric materials were employed to train and test all models. The hybrid neural network–humpback whale optimizer model outperformed other models to predict KOD for all cut materials. The root-mean-square error between predicted and experimental data was 0.349–0.627 µm, 0.085–0.242 µm, and 0.023–0.079 µm for conventional neural network, neural network–particle swarm model, and neural network–humpback whale model, respectively.