The Extreme Learning Machine (ELM) stands out in machine learning as a powerful tool for approximating complex nonlinear mappings. However, challenges arise from the inherent randomness in weight initialization, impac...
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The modular multilevel converter (MMC) arm current contain AC current, DC current, and circulating current. Therefore, by controlling the arm current, the control structure can be simplified and indirect suppression o...
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Aiming at the problem that the traditional PID controller was not ideal, the parameters could not be adjusted to the best state, and the control system could not achieve good control effect, an improved seagull optimi...
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ISBN:
(数字)9781665408530
ISBN:
(纸本)9781665408530;9781665408523
Aiming at the problem that the traditional PID controller was not ideal, the parameters could not be adjusted to the best state, and the control system could not achieve good control effect, an improved seagull optimization algorithm (SOA) based on improved Sine chaotic mapping was proposed to optimize the parameters of PID controller. Sine mapping strategy was adopted to make the initial seagull population evenly distributed in the search space, to improve the shortcomings of the seagull optimization algorithm, such as low solution accuracy, slow convergence speed and easy to fall into premature convergence, and improve the convergence speed and convergence accuracy of the algorithm. Eight standard test functions were tested, and the improved gull optimizationalgorithm was compared with the unimproved gull algorithm, particle swarm optimizationalgorithm (PSO), beetle antennae search algorithm (BAS), particle swarm optimization -beetle antennae search algorithm (PSO-BAS) and the seeker optimizationalgorithm (TSOA), to verify that the improved gull optimizationalgorithm has better optimization effect. The improved algorithm is applied to a second-order system and double closed-loop DC motor speed regulation system to optimize the parameters of PID controller. The results show that the algorithm has high precision, simple principle, better convergence precision and faster convergence speed.
In this paper, fractional order PID (FOPID) optimized with seagull optimization algorithm (SOA) is designed as a secondary regulator for load frequency control (LFC) of the interconnected power system (IPS). However, ...
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ISBN:
(纸本)9781665469258
In this paper, fractional order PID (FOPID) optimized with seagull optimization algorithm (SOA) is designed as a secondary regulator for load frequency control (LFC) of the interconnected power system (IPS). However, the efficacy of FOPID is showcased with other integral order type classical controllers. IPS deliberated in this work is a widely used model of dual area hydro-thermal (DAHT) upon laying 10% perturbation of step load on area-1 (10%SLP) for investigation. Further, plug-in-electric vehicles (PEVs) are integrated into area-1 of DAHT for analysis purposes. DAHT responses are enhanced with the integration of PEVs. Later, the control areas of DAHT are incorporated with superconducting magnetic energy storage (SMES) devices to get further enhancement in system stability. Finally, simulation results reveal the superiority of implementing PEVs and SMES coordinated strategies for improvement in frequency regulation.
Existing methods in predicting short-term photovoltaic (PV) power have low accuracy and cannot satisfy actual demand. Thus, a prediction model based on similar days and seagull optimization algorithm (SOA) is proposed...
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Existing methods in predicting short-term photovoltaic (PV) power have low accuracy and cannot satisfy actual demand. Thus, a prediction model based on similar days and seagull optimization algorithm (SOA) is proposed to optimize a deep belief network (DBN). Fast correlation-based filter (FCBF) method is used to select a meteorological feature set with the best correlation with PV output and avoid redundancy among meteorological factors affecting PV output. In addition, a comprehensive similarity index combining European distance and gray correlation degree is proposed to select the similar day. Then, SOA is used to optimize the number of neurons and the learning rate parameters in DBN. Based on the nonuniform mutation and opposition-based learning method, an improved seagull optimization algorithm (ISOA) with higher optimization accuracy is proposed. Finally, the ISOA-DBN prediction model is established, and the experimental analysis is conducted using the actual data of PV power stations in Australia. Results show that compared with DBN, support vector machine (SVM), extreme learning machine (ELM), radial basis function (RBF), Elman, and back propagation (BP), the mean absolute percentage error indicator of ISOA-DBN is only 1.512% on a sunny day, 5.975 on a rainy day, 3.359 on a cloudy to sunny day, and 1.911% on a sunny to cloudy day. Therefore, the good accuracy of the proposed model is verified.
To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the ...
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To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm-back propagation (ISOA-BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks.
This paper introduces a novel control strategy of fractional order (FO) fuzzy (F) PID (FOFPID) controller optimized with the latest soft computing technique of seagull optimization algorithm (SOA) for power system fre...
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This paper introduces a novel control strategy of fractional order (FO) fuzzy (F) PID (FOFPID) controller optimized with the latest soft computing technique of seagull optimization algorithm (SOA) for power system frequency regulation. Initially, a simple and widely accepted power system of dual area photovoltaic (PV) and reheat thermal (RT) (PVRT) system is perceived and named as test system-1 in this paper. The performance of FOFPID fine-tuned with SOA mechanism is tested on PVRT system for a step load disturbance of 10% (SLD) on area-2 along with other controllers reported in the literature. Dynamical analysis of the PVRT system reveals the potency of the proposed controller over others. Further, the SOA based FOFPID controller is extended to frequency regulation of multi-area system with hybrid generating sources (MAHS) named as test system-2 in this paper for 10% SLD on area-1. MAHS system is constituted with realistic constraints to conduct research close to realistic practice. The potentiality of SOA based FOFPID is demonstrated by comparing it with traditional controllers of PID/FPID/FOPID on MAHS system. Finally, robustness analysis is perpetuated to reveal the presented control scheme robustness.
Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling...
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Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R2 and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model.
Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot descri...
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Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets. Second, an improved seagull optimization algorithm was used to optimize and transform the Gaussian process regression model, and a sub-prediction model was established. Finally, the fusion strategy was determined according to the connectivity between the test samples and local sample subsets. The proposed soft sensor model was applied to the prediction of key biochemical parameters of the marine lysozyme fermentation process. The simulation results show that the proposed soft sensor model can effectively predict the key biochemical parameters with relatively small prediction errors in the case of limited training data. According to the results, this model can be expanded to the soft sensor prediction applications in general nonlinear systems.
The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient metho...
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The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient method to effectually share and store data in the cloud server. At the same time, deep learning (DL) models can be applied for effective decision-making in the cloud-based healthcare system. With this motivation, this study develops an Intelligent DD with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System (IDDTLC-CHS) model. The presented IDDTLC-CHS model aims to accomplish effective DD and classifi-cation in the cloud-enabled healthcare environment. For DD, the neighbourhood correlation sequence (NCS) algorithm is employed which generates optimum code words and is then compressed by Deflate model. Besides, the data classification module involves a series of processes namely fuzzy c-means (FCM) segmentation, Xception feature extraction;bidirectional gated recurrent unit (BiGRU), and seagull optimization algorithm (SGO). The SGO algorithm is applied for optimal adjustment of the parameters involved in the BiGRU model. To assess enhanced outcomes of the IDDTLC-CHS model, a wide-ranging simulation analysis is carried out using the benchmark dataset. The comparative analysis reported the betterment of the IDDTLC-CHS model compared to other recent approaches.
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