Neural Network Optimization Algorithm Research Articles

RETRACTED ARTICLE: Network security threat detection technology based on EPSO-BP algorithm

With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.

Hybrid convolutional neural network and Flexible Dwarf Mongoose Optimization Algorithm for strong kidney stone diagnosis

This study presents a novel approach to kidney stone diagnosis using convolutional neural networks (CNN), applied to computed tomography (CT) images. The research addresses the challenge of data imbalance and protocol variation in medical imaging, which often leads to poor generalization of deep learning models. The model first uses three preprocessing techniques to enhance the quality of raw images and increase their quantity for effective CNN training. The main idea is to optimize the main arrangement of the convolutional neural network based on the proposed flexible version of dwarf mongoose optimization (FDMO) algorithm to provide a good detector model in kidney stone diagnosis. The model is then trained and tested on images from “CT Kidney Dataset”, and its comparison results with some other published works demonstrates its robustness and ability to generalize. The results indicate a significant improvement in diagnostic accuracy, potentially minimizing physician-induced errors and enhancing patient care.

Optimization of medium components for protein production by Escherichia coli with a high-throughput pipeline that uses a deep neural network

To optimize rapidly the medium for green fluorescent protein expression by Escherichia coli with an introduced plasmid, pRSET/emGFP, a single-cycle optimization pipeline was applied. The pipeline included a deep neural network (DNN) and mathematical optimization algorithms with simultaneous optimization of 18 medium components. To evaluate the DNN data sampling method, two methods, orthogonal array (OA) and Latin hypercube sampling (LHS), were used to design 64 initial media for each sampling method. The OA- and LHS-based data sampling resulted in green fluorescent protein fluorescence intensities of 0.088× 103-1.85× 104 and 3.30× 103-1.50× 104, respectively. Fifty DNN models were built using the OA and LHS datasets. Hold-out validation was performed using 15% test of OA and LHS data. Mean square errors of the DNN models were 0.015-0.64, indicating the estimation accuracies were sufficient. However, the sensitivities of components in the DNN models varied and were grouped into six major classes by the index of k-means clustering. A representative model was selected for each class. Mathematical optimization algorithms using Bayesian optimization and genetic algorithm were applied to the representative models, and representative optimized medium (OM) compositions were selected by k-means clustering from the proposed OMs. A total of 54 OMs were obtained from the OA and LHS datasets. In the validating cultivation, the best OMs of OA and LHS were 2.12-fold and 2.13-fold higher, respectively, than those of the learning data.

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%.

Neural network-based modeling of solid oxide fuel cells for marine applications

As the solid oxide fuel cell (SOFC) experimental test is still quite cost-effective and time-consuming, there is a growing need for developing effective simulation tools to reduce the time and cost of the marine SOFC performance test and optimization. The present paper is aimed to study the modeling and simulation of marine solid oxide fuel cells by artificial intelligence method. A neural network based on a particle swarm optimization algorithm is used to establish a marine solid oxide fuel cell model for voltage/current characteristic analysis. The model is also compared with BP neural network and Hopfield neural network methods. The simulation results compared with experimental data show that the effectivity of the particle swarm optimization neural network algorithm is best, which can accurately predict the voltage/current characteristic curves of a SOFC under different fuel flow-air volume ratios. The model study can provide support for SOFC performance characteristics analyses and has significant potential in SOFC optimization applications.

Electric vehicle charging design: The factored action based reinforcement learning approach

Charging optimization design for Electric Vehicles (EV) is challenging because it must account for various uncertainties and design aspects such as random EVs’ arrivals and departures, battery degradation, and transformer Loss of Life (LoL). Model-free reinforcement learning (RL) can be employed to tackle such the EV charging design where it does not require to explicitly model the environment dynamics and accurately predict relevant system parameters. However, the high complexity involved in conventional RL-based approaches usually limits its application to only small-scale EV charging settings, which is impractical. To overcome this limitation, we employ the factored action based RL method to transform the formulated Markov Decision Process (MDP). Then, we propose novel reward shaping and hybrid learning methods combining the Convolutional Neural Network (CNN) and Proximal Policy Optimization (PPO) algorithm to extract relevant features from high-dimension state space and efficiently solve the transformed MDP problem. Extensive numerical studies demonstrate that the proposed design can be used to control a charging station (CS) supporting a large number of EVs. Moreover, we show that the proposed framework greatly outperforms other baselines including single-agent and multi-agent RL based strategies and a heuristic power scheduling algorithm in terms of the achieved reward.

Radial basis function neural network optimization algorithm based on dynamic inertial weight particle swarm optimization for separating overlapping peaks in ion mobility spectrometry.

Ion mobility spectrometry (IMS), as a promising analytical tool, has been widely employed in the structural characterization of biomolecules. Nevertheless, the inherent limitation in the structural resolution of IMS frequently results in peak overlap during the analysis of isomers exhibiting comparable structures. The radial basis function (RBF) neural network optimization algorithm based on dynamic inertial weight particle swarm optimization (DIWPSO) was proposed for separating overlapping peaks in IMS. The RBF network structure and parameters were optimized using the DIWPSO algorithm. By extensively training using a large dataset, an adaptive model was developed to effectively separate overlapping peaks in IMS data. This approach successfully overcomes issues related to local optima, ensuring efficient and precise separation of overlapping peaks. The method's performance was evaluated using experimental validation and analysis of overlapping peaks in the IMS spectra of two sets of isomers: 3'/6'-sialyllactose; fructose-6-phosphate, glucose-1-phosphate, and glucose-6-phosphate. A comparative analysis was conducted using other algorithms, including the sparrow search algorithm, DIWPSO algorithm, and multi-objective dynamic teaching-learning-based optimization algorithm. The comparison results show that the DIWPSO-RBF algorithm achieved remarkably low maximum relative errors of only 0.42%, 0.092%, and 0.41% for ion height, mobility, and half peak width, respectively. These error rates are significantly lower than those obtained using the other three algorithms. The experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS.

Prediction of Transformer Oil Temperature Based on an Improved PSO Neural Network Algorithm

Abstract: In addressing the issue of power transformer oil temperature prediction, traditional back propagation (BP) neural network algorithms have been found to suffer from local optimization and slow convergence. This study proposes an oil temperature prediction model based on an improved particle swarm optimization (PSO) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA) optimization neural network, and the improved PSO neural network are compared by considering various factors, such as ambient temperature, load changes, and the number of cooler groups under different working conditions. Results show that the proposed algorithm improves the actual change trend of oil surface temperature and makes the transformer operation more stable to a certain extent. Background: The mathematical model for predicting transformer oil temperature is clear, but the parameters in the model are uncertain and vary with time. When subjected to different operating conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system parameters. Objective: This paper aims to enhance the accuracy of transformer temperature prediction. In order to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter requirements. Methods: The paper utilizes an oil temperature prediction model based on an improved PSO neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard PSO algorithm. Results: This paper has employed a fusion algorithm of the genetic algorithm of the BP neural network and the PSO algorithm, and conducted simulation and experimental analysis. The simulation and experimental results demonstrate the accuracy and effectiveness of the fusion algorithm. Conclusion: This study demonstrates enhanced prediction accuracy of transformer oil surface temperature using the improved particle swarm optimization neural network algorithm. This algorithm has less prediction error under different working conditions compared to other algorithms. By increasing population diversity and combining inertia weights, the algorithm not only greatly improves its search performance but also avoids local optimization.

Design of Bismuth-Doped Fiber Amplifier Based on Neural Network and Multi-Objective Optimization Algorithm

Design of Bismuth-Doped Fiber Amplifier Based on Neural Network and Multi-Objective Optimization Algorithm

A long-term prediction method for PM2.5 concentration based on spatiotemporal graph attention recurrent neural network and grey wolf optimization algorithm

With the rapid advancement of global industrialization and urbanization, the problem of air pollution, primarily attributed to PM2.5, as the main component has become increasingly serious, directly affecting people's health. Therefore, it is necessary to forecast the long-term PM2.5 levels. However, most existing prediction methods for PM2.5 concentration lack sufficient extraction of urban spatial characteristics. Therefore, this paper proposes a novel method based on spatiotemporal graph attention recurrent neural network and grey wolf optimization (GWO-GART). Firstly, a long-term prediction model of PM2.5, the Graph Attention Cycle Network (GART), is constructed by using Graph Attention network (GAT), Graph Neural Network (GNN) and Rated Recurrent Unit (GRU) networks. In the GART model, a novel multi-GAT is proposed for the first time, which can extract the spatial features of cities circularly and obtain more implicit associations between cities. Secondly, we utilize grey wolf optimization (GWO) to select the best set of hyperparameters for the model. This automated approach aids in designing the network structure of GART. We compared GWO-GART with three single models and two hybrid models using a dataset collected from the real world. The results show that, compared with the state-of-the-art (SOTA) models, GWO-GART had an average reduction of 2.13% in RMSE, 2.47% in MAE, and an average increase of 1.55% in CSI on all datasets. This demonstrates that GWO-GART provides an efficient approach to precisely forecast long-term PM2.5 levels in urban regions.

Retracted: Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm

Retracted: Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm

Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization

Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization

Robust optimization of geoenergy production using data-driven deep recurrent auto-encoder and fully-connected neural network proxy

Robust and efficient optimization of post-history well production schedule under history-matched geomodel known as closed-loop production management is crucial to achieve reasonable and reliable decision-making for subsurface geoenergy resources (e.g., oil/gas resources, geothermal, natural gas hydrate, etc.) recovery in petroleum industry. The procedure generally consists of a cycling implementation of geomodel history matching and production schedule optimization through repeatedly simulating high-fidelity reservoir models and therefore needs almost infeasible computation cost particularly for field-scale oil production scenario. Through hybridizing deep recurrent neural network and particle-swarm optimization algorithm, we present a novel data-driven computation framework for robust post-history production optimization without explicitly performing history matching. In the proposed framework, we develop a hybrid neural network proxy model, which combines recurrent autoencoder (RAE) and fully-connected neural network (FCNN), referred to as RAE-FCNN. The RAE structure can extract latent information from time-series history data, and the FCNN structure can map the combination of extracted latent variables by RAE and post-history well-control sequence to net present value (NPV). The trained RAE-FCNN proxy model can predict NPV values corresponding to the history measurements and user-specific post-history well controls. In the robust optimization framework, the averaged NPV over an ensemble of noisy history measurements are regarded as the objective function while the post-history well-controls are chosen as the decision variables. The proposed surrogate-based optimization framework has been demonstrated on two waterflooding reservoir production cases, e.g., a 2D synthetic Gaussian model and a 3D benchmaRk channelized EGG model.The decision variables considered in optimization are the injection rates with linear bound constraints at the injectors. In comparison with the optimization results by high-fidelity reservoir models, solely 1000 high-fidelity reservoir models for both cases are sufficient to train accurate proxy model and achieve satisfactory post-history production optimization results. Our proposed deep-learning-based optimization method can effectively make full use of history data to predict post-history oil well production without the need of computationally expensive history matching step, which enables us to achieve quick decision-making and risk evaluation for close-loop waterflooding reservoir production management and optimization.

Performance optimization of solid oxide electrolysis cell for syngas production by high temperature co-electrolysis via differential evolution algorithm with practical constraints

Co-electrolysis of water and carbon dioxide using solid oxide electrolysis cell combined with the catalytic reactor to produce renewable synthesis fuel is considered as a promising strategy for carbon dioxide utilization. However, in order to achieve a target product with a remarkable system-level efficiency, a certain proportion of carbon monoxide and hydrogen from solid oxide electrolysis cell must be sent to the catalytic reactor directly, which imposes practical constraints on the solid oxide electrolysis cell performance. Take this constraint into consideration, we proposed a performance design methodology that enables a rapid and effective optimization of the solid oxide electrolysis cell operating conditions. Firstly, a physical model is developed by integrating chemical equilibrium, electrochemistry, and energy conservation involved in the solid oxide electrolysis cell. Secondly, a physical model is utilized to generate a dataset for the development of neural network model and optimization algorithm. This approach allows for the efficient determination of optimal conditions while considering practical constraints. In the case study, under the constraint that the desired fuel is a long carbon chain fuel with a carbon dioxide conversion rate ranging from 80 % to 90 %, the highest efficiency achieved by the solid oxide electrolysis cell is optimized to 66.75 %. Moreover, this approach is extended to other two target fuels (dimethyl ether and methane) under different carbon dioxide conversion rate, and the optimal performance is designed. According to the results, there is a trade-off between high efficiency and high carbon dioxide conversion rate, as the low carbon dioxide conversion rate often leads to higher efficiency. This method allows for flexible design of operating conditions to meet different fuel requirements and guide practical applications.

Accelerated fuzzy min–max neural network and arithmetic optimization algorithm for optimizing hyper-boxes and feature selection

Accelerated fuzzy min–max neural network and arithmetic optimization algorithm for optimizing hyper-boxes and feature selection

Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization

Online diagnosis of abnormal temperature is vital to ensure the reliability and operation safety of lithium-ion batteries, and this study develops a hybrid neural network and fault threshold optimization algorithm for their online surface temperature prediction and abnormal diagnosis. To be specific, a hybrid neural network incorporating convolutional neural network and long short-term memory neural network is firstly employed to predict the battery temperature, and a residual monitor is designed to track the deviation between the measure and the prediction. Then, the acquired residual is compared with the fault threshold to diagnose whether the battery temperature is abnormal. Moreover, to improve the correctness and reliability of fault diagnosis, a fault threshold optimization algorithm based on the receiver operating characteristic curve is defined to automatically find the optimal fault threshold. The accuracy and reliability of temperature prediction is verified under various aged state and temperatures, as well as different battery types. The abnormal experiment validation on three datasets reveals that the proposed method can diagnose and unwind temperature fault warning timely and reliably. Additionally, the average execution time of each prediction and diagnosis is less than 3.5 ms, manifesting the real-time application capability of the proposed method.

Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

An exact and quick finding of Covid-19 patients plays a significant part in the initial period of medicinal treatment and prevention. Automatic recognition of COVID-19 cases using CT images may help lessen the effect of this infection on human civilization. In recent years, computer vision is the major solution for diagnosing the covid-19 disease by using CT images. Besides, many researchers had presented efficient artificial intelligence techniques for identifying Covid-19 disease. Nevertheless, the accuracy and time consumption of the model further to be improved. Thus, in this work, we propose a Covid-19 disease classification using a hybrid deep neural network with handcrafted features. The proposed approach consists of three stages namely, pre-processing, handcrafted feature extraction, and classification. Initially, the images are given to the pre-processing stage to remove the noise present in the input images. Then, we extract the hand-crafted features (Gray-Level Co-Occurrence Matrix (GLCM)) from each image. After that, the pre-processed image is given to the input of the optimized deep neural network classifier to classify an image as normal or abnormal. The proposed optimized deep neural network is a combination of a convolution neural network (CNN) and adaptive artificial jelly optimization (A2JO) algorithm. To enhance the performance of classifier accuracy, the extracted handcrafted features are fused with the hybrid deep neural network. The performance of the proposed approach was analyzed based on different metrics and performance compared with other techniques.

Early Warning of Red Tide of Phaeocystis globosa Based on Phycocyanin Concentration Retrieval in Qinzhou Bay, China

Phaeocystis globose (P. glo) are the most frequent harmful algae responsible for red tides in Qinzhou Bay, Guangxi. They pose a significant threat to the coastal marine ecosystem, making it essential to develop an efficient indicator method tailored to P. glo outbreaks. In remote sensing water quality monitoring, there is a strong correlation between P. glo and cyanobacteria, with phycocyanin (PC) serving as an indicator of cyanobacterial biomass. Consequently, existing research has predominantly focused on remote sensing monitoring of medium to high PC concentrations. However, it is still challenging to monitor low PC concentrations. This paper introduced the BP neural network (BPNN) and particle swarm optimization algorithm (PSO). It selects spectral bands and indices sensitive to PC concentrations and constructs a PC concentration retrieval model, in combination with meteorological factors, offering a comprehensive exploration of the indicative role of low PC concentrations in predicting P. glo red tide outbreaks in Qinzhou Bay. The results demonstrated that the PC concentration retrieval model, based on the backpropagation neural network optimized by the particle swarm optimization algorithm (PSO-BPNN), demonstrated better performance (MAE = 0.469, RMSE = 0.615). In Qinzhou Bay, PC concentrations were mainly concentrated around 2~5 μg/L. During the P. glo red tide event, the area with undetectable PC concentrations (PC < 0.04 μg/L) increased by 4.97 km2, with regions below 0.9 μg/L experiencing exponential growth. Considering the variations in PC concentrations along with meteorological factors, we proposed a straightforward early warning threshold for P. glo red tides: PC < 0.9 μg/L and T < 20 °C. This method, from a remote sensing perspective, analyzes the process of P. glo outbreaks, simplifies PC concentration monitoring, and provides a reasonably accurate prediction of the risk of P. glo red tide disasters.

A novel robust adaptive neuro-sliding mode steering controller for autonomous ground vehicles

This paper intends to harness recent advances in intelligent commands to develop a novel steering control strategy for autonomous ground vehicles (AGVs) maneuvering problems. In this research, the vehicle is embodied by a single track-model (ST) taking into consideration its lateral dynamics. Besides, it is subject to unknown disturbances and uncertainties, associated with the tire/road adhesion coefficient, which are upper bounded by a positive limit. An on-line Radial Basis-function Neural-Networks (RBNN) adaptive mechanism is designed to estimate this upper limit in the sense of Lyapunov. On this basis, a novel robust adaptive neuro-sliding mode steering angle controller (AN-SMC) is introduced. Whilst the effectiveness of the controller is impacted by its parameters, a new neural-network optimization algorithm (NNA) is employed to provide optimal values for these settings. The proposed approach is evaluated through a single lane-change-maneuver planned out by a new methodology. The steering controller revealed a powerful path-tracking via the comparisons carried out in support of the developed strategy.

Classification and rapid non-destructive quality evaluation of different processed products of Cyperus rotundus based on near-infrared spectroscopy combined with deep learning

The quality of traditional Chinese medicine is very important for human health, but the traditional quality control method is very tedious, which leads to the substandard quality of many traditional Chinese medicine. In order to solve the problem of time-consuming and laborious traditional quality control methods, this study takes traditional Chinese medicine Cyperus rotundus as an example, a comprehensive strategy of near-infrared (NIR) spectroscopy combined with One-dimensional convolutional neural network (1D-CNN) and chaotic map dung beetle optimization (CDBO) algorithm combined with BP neural network (BPNN) is proposed. This strategy has the advantages of fast and non-destructive. It can not only qualitatively distinguish Cyperus rotundus and various processed products, but also quantitatively predict two bioactive components. In classification, 1D-CNN successfully distinguished four kinds of processed products of Cyperus rotundus with 100 % accuracy. Quantitatively, a CDBO algorithm is proposed to optimize the performance of the BPNN quantitative model of two terpenoids, and compared with the BP, whale optimization algorithm (WOA)-BP, sparrow optimization algorithm (SSA)-BP, grey wolf optimization (GWO)-BP and particle swarm optimization (PSO)-BP models. The results show that the CDBO-BPNN model has the smallest error and has a significant advantage in predicting the content of active components in different processed products. To sum up, it is feasible to use near infrared spectroscopy to quickly evaluate the effect of processing methods on the quality of Cyperus rotundus, which provides a meaningful reference for the quality control of traditional Chinese medicine with many other processing methods.