Neural Network Optimization Algorithm Research Articles

Implication of ICWFPSO as Optimization Neural Network Algorithm on Sales Forecasting System

Predictive systems play a crucial role in a company's operations and strategy by aiding in more informed and data-driven decision-making and more effective planning and budgeting. It is possible to develop an intelligent system to perform forecasting. Neural networks offer significant advantages in forecasting systems due to their flexible modeling capabilities. However, this algorithm's fundamental weakness is the slow convergence rate and being trapped in a local minimum. To overcome it, this research is conducted to optimize the NN algorithm using the ICWFPSO to produce a forecasting algorithm with high accuracy and faster execution time using real e-commerce sales data for the past 7 years. Algorithm performance testing tests the Mean Absolute Error (MAE) value of the forecasting system using three scenarios: the NN forecasting algorithm, the NN optimized with ICWFPSO on the weight value, and the same scheme. Still, the optimized value is the hyperparameter value. ICWPSO has been shown to enhance the performance of PSO by tuning the inertia weight dynamically, which helps balance exploration and exploitation during the optimization process. The best prediction result is obtained when optimizing the hyperparameters using the ICWFPSO optimization technique compared to using traditional NN or optimizing weight value with ICWFPSO with the MAE value of 245.32958984375, and the best performance is obtained at iterations below 100. Further, gradient-based optimization methods might be generally preferred for their efficiency and effectiveness in handling large-scale neural network training.

Analysis and Prediction of Nonlinear Hysteretic Behaviors of Graphite Magnetorheological Grease at Different Temperatures.

Magnetorheological grease (MRG) is considered a promising alternative to magnetorheological fluids as a smart material because of its higher stability and less leakage. To enhance yield stresses in various applications, graphite is incorporated as an additive, resulting in graphite magnetorheological grease (GMRG). However, the nonlinear hysteresis properties of this new material and its prediction methods have not been investigated. Therefore, in this work, the nonlinear hysteretic properties of GMRG at different temperatures, magnetic fields, and frequencies are systematically investigated and compared with those of MRG. The results of rheological experiments show that graphite enhances the shear stress of GMRG in the hysteresis curve through its reinforcing effect on the magnetic chains. The strain hardening, elasticity, and viscosity of GMRG are enhanced, but an increase in temperature decreases this efficacy. A prediction model based on the particle swarm optimization neural network algorithm (PSO-BP) is also proposed to efficiently and accurately control the nonlinear behavior of GMRG in engineering. The hysteresis curves of GMRG under different external excitations are characterized and predicted by the particle swarm optimization neural network approach. The statistical results show that the PSO-BP-based hysteresis characteristic estimates have satisfactory accuracy. In the test data, the PSO-BP model demonstrates improvements in RMSE, MAE, and SMAPE by 19.5%, 20.6%, and 21.0%, respectively, compared to the conventional BP network, which maintains an R2 value exceeding 0.95. This method provides an efficient solution for researchers to carry out reliable performance prediction in work such as genetic engineering of materials and related engineering applications.

Experimental analysis and low-damage machining strategy for composite ultrasonic vibration-assisted grinding of silicon carbide based on DA-MLP-NSGA-II algorithm

At present, because of the lack of ultrasonic composite vibration assisted grinding mechanism, neural network optimization algorithm (NNOA) is used to optimize the processing results. In NNOA, multi-layer perceptron (MLP) neural network model and non-dominated sorting genetic algorithm-II (NSGA-II) are very efficient and accurate methods. In this paper, based on the measurement and analysis of the specific ultrasonic vibration device, the CUVAG experiments on silicon carbide (SiC) ceramic were carried out to investigate the influence of processing parameters on the grinding forces, the ground surface roughness and morphology, and the subsurface damage. Then, the brittle-ductile removal behavior of hard-and-brittle materials could be revealed according to the above analysis. After that, MLP model and NSGA-II were utilized to predict and optimize the processing results in CUVAG. The results show that the grinding forces are basically constant, the surface quality deteriorates, and the subsurface damage increases with increased axial vibration amplitude and workpiece infeed speed, but all fluctuate with enlarged wheel speed, and turns at the inflection point of brittle-ductile transition with increased elliptic vibration amplitude. The fitting goodness R2 of the established MLP neural network prediction model is between 0.94 and 0.975, and the process parameters calculated by the NSGA-II optimization algorithm are verified. With optimized processing parameters, the grinding forces are reduced by about 13 %, the surface roughness is reduced to Ra0.037 μm (by 29 %), and the depth of subsurface damage is reduced by 68 %.

Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning

The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.

Rapid and precise calibration of soil microparameters for high-fidelity discrete element models in vehicle mobility simulation

In the realm of numerical simulations concerning vehicle mobility, the establishment of a high-fidelity soil discrete element model often necessitates substantial parameter adjustments to align with the mechanical responses of actual soil. In pursuit of a rapid and precise calibration of the microparameters of the soil model, this paper describes an approach rooted in machine learning surrogate models. This method calibrates the corresponding discrete element microparameters based on the macroscopic Mohr–Coulomb parameters derived from actual soil direct shear tests. The distinct contribution lies in the creation of a dataset that bridges the soil microparameters and macroparameters through simulated direct shear tests, which serves as training data for machine learning algorithms. Additionally, an adaptive particle swarm optimization neural network algorithm is proposed to represent the nonlinear relationships among parameters within the dataset, thus achieving intelligent calibration. To verify the reliability of the proposed soil calibration model in the context of vehicle mobility simulations, a co-simulation is conducted using a vehicle multibody dynamics simulation model and the calibrated soil model, with validation conducted across multiple criteria.

Email Spam Detection Using a Hybrid Approach of Feedforward Neural Network and Penguin Optimization Algorithm

The electronic communication of today is beset by unwanted junk messages. These are not just a nuisance; they waste our time and energy and clog up our inboxes, which are supposed to be, in many ways, our digital front doors. But how do we keep them from coming? Well, as today's article makes clear, the situation is hardly straightforward. There are various methods that can be used, both traditional and modern, and each has its own set of pros and cons. One method, however, stands out in today's article as one that we think might work well in practice, the good old-fashioned neural network. The method's novelty derives from its use of neural networks, a type of machine learning, in conjunction with the POA. The POA is a powerful optimization algorithm that can perform very well when fine-tuning the weights and biases of a neural network. We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. This figure surpasses the next nearest competitor by 2% and demonstrates the efficacy of our method in spam detection while also highlighting the POA's potential in this field. The overall proposed method pushes the spam-detection field forward, but it also demonstrates something even broader: the power of hybrid approaches in machine learning. Neural networks are great—but you have to train them properly, and the use of a particle swarm for that purpose is a neat idea. But in the end, a hybrid spam filter is still a spam filter, and this one does what it's supposed to do better than many rivaling methods.

Optimization of renewable energy hybrid power plant in the state of Karnataka, India

This paper proposes a methodology on reliability and cost minimization of the electricity produced by Renewable energy sources. The approach is to use Hybrid Renewable Energy Systems, which involves integrating various renewable sources. The objective of this study is to address these challenges by HRES method in the state of Karnataka. In Karnataka, there has been large scale curtailment of renewable energy generation due to the various structural issues. This study would give a solution to reliability and cost related issues faced by this state. For the study a Mathematical Model has been developed using Feed Forward Neural Network (FNN) and Chemical Reaction Optimization algorithms. Chemical reaction Optimization model is chosen to remove biases of the Artificial Neural Network Model of FNN. This model will be used to simulate interaction between Hybrid Renewable Energy Systems. The result of the study will provide a power system with high & optimized renewable energy output in the state of Karnataka, India which will be reliable and cost effective.

Application of multi-objective neural network algorithm in industrial polymerization reactors for reducing energy cost and maximising productivity

Optimization on an industrial scale is a complex task that involves fine-tuning the performance of large-scale systems and applications to make them more efficient and effective. This process can be challenging due to the increasing volume of work, growing system complexity, and the need to maintain optimal performance. Due to the significant power required for compression and the high costs of reactant materials, optimizing low-density polyethylene (LDPE) production to provide maximum productivity with a reduction of energy cost is required. However, it is not a simple process because the optimization problem of the LDPE tubular reactor consists of conflicting objective functions. Multi-objective neural network algorithm (MONNA) is a metaheuristic optimization method that provides a versatile and robust approach for solving complex, contradictory targets and diverse optimization problems that do not rely on specific mathematical properties of the problem. It is inspired by the structure and information-processing capabilities of biological neural networks. MONNA iteratively proposes solutions, evaluates its performance, and adjusts its approach based on feedback, which avoids complex mathematical formulations. In this work, we implement Multi-objective optimization neural network algorithm (MONNA) in LDPE tubular reactor for maximising productivity, conversion and minimising energy costs with three scenario of problem optimization, i.e. maximising productivity and reducing energy cost for the first problem (P1); increasing conversion and reducing energy costs for the second problem (P2); and increasing productivity and reducing by-products for the third problem (P3). The results show that the highest productivity, highest conversion, and lowest energy are 545.1 mil. RM/year, 0.314, and 0.672 mil. RM/year. The extreme points in the Pareto Front (PF) for various bi-objective situations provide practitioners with helpful information for selecting the best trade-off for the operational strategy. According to their preferences, decision-makers can use the resulting Pareto to decide on the most acceptable alternative. The decision variable plots show that both initiators in the reacting zone highly affected the optimal solution with the opposite action.

Optimization control of wastewater treatment based on neural network and multi-objective optimization algorithm

In wastewater treatment, energy consumption and effluent quality are conflicting objectives. Improving effluent quality while reducing energy consumption has become a pressing issue. This paper proposes a dynamic optimal control method for the wastewater treatment process, utilizing neural networks and multi-objective dragonfly algorithm. A regression prediction model, based on a Self-Attention mechanism of a multi-scale convolutional neural network-bidirectional gated recurrent unit, is used to establish a soft measurement model for effluent quality and energy consumption, serving as the optimization objective function. The multi-objective dragonfly algorithm then optimizes this objective to determine the optimal set values for control variables. A Linear Active Disturbance Rejection Control, based on a scalable bandwidth linear extended state observer, is designed to track these optimal set values. The proposed method is tested using Benchmark Simulation Model No. 1 (BSM1). Results demonstrate that the method effectively obtains and tracks optimal set values of control variables, ensuring effluent quality meets standards and significantly reducing energy consumption compared to four other methods.

Maximizing solar energy efficiency with efficient interleaved boost converter with Three-Level neutral point clamped inverter for Grid-Connected photovoltaic using hybrid approach

The efficient interleaved boost converter (IBC) combined with the 3-level neutral point clamped (NPC) inverter for grid-connected photovoltaic systems (GCPVS) maximizes solar energy efficiency are presented. The proposed hybrid technique implies the combination of Double Attention Convolutional Neural Network (DACNN) and Starling Murmuration Optimization (SMO) algorithm and is usually referred as DACNN-SMO technique. Enhancing power quality at the Point of Common Coupling (PCC) while utilizing the rated capacity of the inverter into consideration is the main goal of the proposed approach. The grid-connected photovoltaic system additionally includes an efficient three-level NPC inverter and interleaved boost converter to decrease DC link voltage oscillation. In addition to preventing overheating, the inverter current controls reactive power adjustment, active power injection, and current harmonic filtering. Utilizing the SMO technique, By employing the SMO technique, the system effectively regulates output voltage, ensuring that the inverter output voltage. The DACNN enhances the system’s ability to monitor and diagnose faults. Using MATLAB, the proposed topology is implemented, and the results are contrasted with those of other existing techniques. The existing methods such as Heap Based Optimizer (HBO), Circle Search Algorithm (CSA) and Sparrow Search Algorithm (SSA) attains a higher THD of 3.7%, 4.7% and 5.3% respectively. The proposed method DACNN-SMO attains a THD of 2.5%. The proposed technique displays the efficiency is 99.86%. When compared to existing strategies, the proposed technique displays lower THD and high efficiency.

Optimization of Logistics Distribution Network based on Ant Colony Optimization Neural Network Algorithm

Optimization of Logistics Distribution Network based on Ant Colony Optimization Neural Network Algorithm

Physical Delivery Network Optimization Based on Ant Colony Optimization Neural Network Algorithm

The development of modern logistics chains is not just simple cargo transportation, it has become a cross-integrated industry that integrates many emerging technologies such as IoT technology, intelligent transportation, cloud computing and mobile Internet. Based on the ant colony algorithm (ACA), this paper optimizes the physical delivery network of the optimized neural network algorithm, establishes a mathematical model for the constraints and optimization objectives in the optimization of the physical delivery path, and proposes some improvements to the ACA to improve the convergence of the algorithm. speed and global search ability, so as to use the improved algorithm to solve the physical delivery path optimization problem. Experiments show that the optimal distance of physical delivery path planning calculated by traditional ACA is 207.8544km, while the optimal distance of improved ACA path planning is 197.9879km. The performance of the improved ACA is improved by analyzing the results of solving typical examples.

Fault causes and its detection in standalone PV system using ANN and GEO technique

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

A prediction method for salt frost resistance of carbon-glass mixed fiber concrete based on improved BP neural network

Abstract Low temperature and freeze-thaw can greatly reduce the service life of concrete, leading to serious safety hazards. Salt frost resistance is one of the important characteristics of concrete. Therefore, in order to more accurately evaluate the salt frost resistance of carbon glass hybrid fiber reinforced concrete. This article proposes a prediction method using measured data based on an improved BP neural network. Firstly, conduct freeze-thaw cycle tests on fiber reinforced concrete with different proportions. Studied the variation law of concrete quality loss rate and dynamic elastic modulus. Then, based on BP neural network and particle swarm optimization algorithm, a salt frost damage prediction model for carbon glass hybrid fiber reinforced concrete was established. Finally, conduct optimization based on the learning samples and compare and analyze the prediction errors of the model. The simulation results demonstrate that the proposed method has good accuracy and stability.

Software cost estimation predication using a convolutional neural network and particle swarm optimization algorithm

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation.

A Short-Term Air Pollutant Concentration Forecasting Method Based on a Hybrid Neural Network and Metaheuristic Optimization Algorithms

In the contemporary era, global air quality has been adversely affected by technological progress, urban development, population expansion, and the proliferation of industries and power plants. Recognizing the urgency of addressing air pollution consequences, the prediction of the concentration levels of air pollutants has become crucial. This study focuses on the short-term prediction of nitrogen dioxide (NO2) and sulfur dioxide (SO2), prominent air pollutants emitted by the Kerman Combined Cycle Power Plant, from May to September 2019. The proposed method utilizes a new two-step feature selection (FS) process, a hybrid neural network (HNN), and the Coot optimization algorithm (COOT). This combination of FS and COOT selects the most relevant input features while eliminating redundant ones, leading to improved prediction accuracy. The application of HNN for training further enhances the accuracy significantly. To assess the model’s performance, two datasets, including real data from two different parts of Combined Cycle Power Plant in Kerman, Iran, from 1 May 2019 to 30 September 2019 (namely dataset A and B), are utilized. Subsequently, mean square error (MSE), mean absolute error (MAE), root mean square deviation (RMSE), and mean absolute percentage error (MAPE) were employed to obtain the accuracy of FS-HNN-COOT. Experimental results showed MSE of FS-HNN-COOT for NO2 ranged from 0.002 to 0.005, MAE from 0.016 to 0.0492, RMSE from 0.0142 to 0.0736, and MAEP from 4.21% to 8.69%. Also, MSE, MAE, RMSE, and MAPE ranged from 0.0001 to 0.0137, 0.0108 to 0.0908, 0.0137 to 0.1173, and 9.03% to 15.93%, respectively, for SO2.

A damage prediction method for building concrete based on improved BP neural network

Concrete is widely used in civil engineering, and fiber materials are usually added to building concrete to enhance its durability. In order to study the freeze-thaw damage performance of different fiber reinforced concrete mixtures and accurately predict the degree of freeze-thaw damage of fiber reinforced concrete with different ratios under salt freezing conditions, this paper proposes an improved BP neural network-based method for predicting freeze-thaw damage in building concrete. Firstly, freeze-thaw cycle tests were conducted on fiber reinforced concrete with different proportions to study the variation patterns of concrete quality loss rate and dynamic elastic modulus; Then, based on BP neural network and particle swarm optimization algorithm, a concrete damage prediction model is established; Finally, conduct optimization based on the learning samples and compare and analyze the prediction errors of the model. The method proposed in this article has been verified to have good accuracy and stability.

A prediction method for salt frost resistance of dam rock foundation based on improved BP neural network

Hydraulic resources can be utilized in a cascade manner, with the advantages of energy conservation, economy, and environmental protection. In order to study the anti salt freezing performance of rock foundation in hydroelectric power dams and accurately predict the degree of rock and soil freeze-thaw damage under salt freezing conditions, this paper proposes a prediction method for the anti salt freezing performance of dam rock foundation based on an improved BP neural network. Firstly, freeze-thaw cycle tests were conducted on the rock foundation materials of dams poured with different proportions of fiber reinforced concrete to study the changes in soil mass loss rate and dynamic elastic modulus; Then, based on BP neural network and particle swarm optimization algorithm, a prediction model for rock and soil freeze-thaw damage is established; Finally, using the historical data of the Baihetan hydropower station, optimization was carried out and model prediction errors were compared and analyzed. The method proposed in this article has been verified to have good accuracy and stability, providing guidance for the operation and construction design of hydropower projects.

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.

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.