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IEEE International Conference in Power Engineering Application (ICPEA)

作者： Kusuma, Vita Priyadi, Ardvono Budi, Avian Lukman Setya Putri, Vita Lystianingrum Budiharto Inst Teknol Sepuluh Nopember Dept Elect Engn Surabaya Indonesia

ISBN: (纸本)9781728185460

Photovoltaic (PV) is a generator that utilizes solar energy into electrical energy. On-grid photovoltaic is the system can reduce electricity bills, besides that the electricity produced is environmentally friendly and free emission. PV power output intermittent depending on weather conditions. Therefore, this research will predict the power output PV one day ahead using Recurrent Neural Network (RNN) method with bayesian regularization algorithm because it can solve problems regarding prediction, classification, and energy management. The measure of accuracy error from the simulation result in this study is calculated using Mean Absolute Percentage Error (MAPE). The PV power forecasting accuracy using RNN method is compared with actual data. The amount of load power that PV cannot fulfill will later be back up by the grid. The prediction of PV power using RNN method with 4 neuron hidden layers and learning rate 0.01 resulted in the best MAPE value of 2,2784 %. Based on the results, PV power forecasting output using the RNN method with historical data can be applied to determine the amount of PV power for day ahead.

关键词： Power Forecasting Recureent Neural Network bayesian regularization algorithm Photovoltaic Mean Absolute Percentage Error

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Short-Term Photovoltaic Power Prediction Based on bayesian Regularized BP Neural Networks 6

Short-Term Photovoltaic Power Prediction Based on Bayesian R...

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6th International Conference on Electrical Engineering and Green Energy (CEEGE)

作者： Liu, Jiakang Wang, Haiyan Hao, Tianfei Xian Univ Technol Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9798350312669

In view of the problem that the randomness and volatility of photovoltaic power generation lead to low prediction accuracy, this paper proposes a short-term prediction method for photovoltaic power generation power based on bayesian regularization algorithm to optimize BP neural network, and analyzes the factors affecting photovoltaic power generation power prediction model for photovoltaic power generation was established, which was trained in neural networks by bayesian regularization algorithm optimization, L-M optimization algorithm and traditional gradient drop method, and the prediction results of the three methods were compared with the study, and the experimental results showed that the method proposed in this paper was effective Improved photovoltaic power prediction accuracy, which can be used for short-term accurate prediction of photovoltaic power.

关键词： photovoltaic power prediction BP neural network L-M optimization algorithm bayesian regularization algorithm

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Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and bayesian regularization algorithms

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MATERIALS 2021年第11期14卷 2998页

作者： Zhang, Xinyong Sun, Liwei Chinese Acad Sci Shanghai Inst Tech Phys Shanghai 200083 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shanghai Inst Tech Phys Key Lab Infrared Detect & Imaging Technol Shanghai 200083 Peoples R China

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure's mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure's mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

关键词： space camera supporting structure optical machine structure optimization backpropagation neural network particle swarm optimization bayesian regularization algorithm

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Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review

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FUEL 2023年第1期337卷

作者： Liang, Bin Liu, Jiang You, Junyu Jia, Jin Pan, Yi Jeong, Hoonyoung Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Peoples R China Chongqing Univ Sci & Technol Chongqing 401331 Peoples R China Res Inst Petr Explorat & Dev Beijing 100086 Peoples R China Seoul Natl Univ Res Inst Energy & Resources Inst Engn Res Dept Energy Syst Engn Seoul 08826 South Korea

Accurate prediction of hydrocarbon production is crucial for the oil and gas industry. However, the strong heterogeneity of underground formation, the inconsistency in oil-gas-water distribution, and the complex flow mechanisms make hydrocarbon production forecasting (HPF) difficult, which leads to a high level of uncertainty in the prediction results. The explosion of machine learning (ML) methodologies that are capable of analyzing big data shed new light on HPF using production data. In this article, an in-depth review is provided regarding HPF using ML methodologies. Firstly, the merits and drawbacks of traditional HPF methods are analyzed and summarized. Then, the applications of ML algorithms in HPF are reviewed in detail, especially concentrating on artificial neural network, support vector machine, and ensemble learning. For each algorithm, the basic theory and its variants are first introduced, and its applications in HPF are comprehensively demonstrated subsequently. Finally, this article presents the challenge and prospects of machine-learning-based HPF. Sophisticated ML proxy models can be con-structed and employed to deal with an extended type of input data such that improving the efficacy of data utilization. On the other hand, deep learning models designed to handle time-series data can gain more attention. Modeling approaches for multivariate time-series hydrocarbon production data using deep neural networks with similar functionality to LSTM may lead to more accurate and computationally efficient production forecasting.

关键词： AI Artificial intelligence ANN Artificial neural network SVM Support vector machine DCA Decline curve analysis MFNN Multilayer feed-forward neural networks MLP Multilayer perceptron BP Back Propagation algorithm DNN Deep neural network LSTM Long short-term memory GAN Generative adversarial network CNN Convolutional neural network FCNN Fully connected neural network RNN Recurrent neural network BPANN Back propagation artificial neural network NN Neural network cDC-GAN Conditional deep convolutional generative neural network CONV-1D 1D convolutional neural network GA Genetic algorithm DE Differential evolution SA Simulated annealing GSA Gravitational search algorithm PSO Particle swarm optimization MPA Marine predator algorithm ICA Imperialist competitive algorithm LM Levenberg Marquardt BRA bayesian regularization algorithm MNN Multiple-neural-network HONN Higher-order neural network HOSO Higher-order synaptic operations MLMVN Multilayer neural network with multi-valued neurons DLSTM Deep long-short term memory EEMD Ensemble empirical mode decomposition FNN Feed-forward neural network BFGS Quasi-newton algorithm ORF Oil recovery factor GOR Gas oil ratio ADAM Adaptive moment estimation method WOPR well oil production rate SGD stochastic gradient descent SVR Support vector regression LSSVM Least squares support vector machine RSM Response surface method LR Linear regression RF Random forest AdaBoost Adaptive boosting GBDT Gradient boosting decision tree GBM Gradient-boosting machine MORF Multi-objective random forest GBT Gradient boosting tree PCA Principal component analysis RREE Reverse recursive feature elimination

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Hybrid algorithm Based On Levenberg-Marquardt bayesian regularization algorithm and Genetic algorithm

Hybrid Algorithm Based On Levenberg-Marquardt Bayesian Regul...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Song, Feng Wang, Hongchun Chongqing Normal Univ Dept Math Chongqing Peoples R China

ISBN: (纸本)9781479925186;9781479925193

In order to overcome the insufficiencies of the convergence of the low speed, a low precision of the forecast and easy convergence to a local minimum point of error function on BP Neural Networks (BPNN), a new hybrid algorithm-LMBRGA, which uses both the Levenberg-Marquardt(LM) bayesian regularization algorithm(LMBRA) and Genetic algorithm(GA) to optimize BPNN, is proposed. The specific process was as follows. Firstly, the GA optimized the best weights and thresholds as the training initial values of BPNN. Then, the BPNN after initialization was trained by the LMBRA until the network has converged. Finally, the network model, which met the requirements after being examined by the test samples, was applied to predict the resident consumption level of Chengdu. By Simulation Experiments analysis, the LMBRGA hybrid algorithm has faster convergence rate than the LMBRA. From the average relative forecasting error (ARFE)'s comparison of the predictive results, it clearly indicates that the forecast precision of the LMBRGA hybrid algorithm is higher than another five optimization algorithms.

关键词： BPNN LM algorithm bayesian regularization algorithm GA

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Backpropagation artificial neural network-based maximum power point tracking controller with image encryption inspired solar photovoltaic array reconfiguration

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期136卷

作者： Kumaraswamy, Madavena Naik, Kanasottu Anil Natl Inst Technol Warangal Dept Elect Engn Warangal India

The enhancement of photovoltaic (PV) arrays through reconfiguration presents a promising avenue for increasing the global maximum power (GMP) and improving overall array performance. This enhancement is achieved by minimizing differences between rows, thereby reducing the computational load on Maximum Power Point Tracking (MPPT) systems. However, many existing reconfiguration methods face various challenges, including scalability issues, inadequate shading dispersion, distortion of array characteristics, emergence of multiple power peaks, increased mismatch, and more. In order to overcome these obstacles, this study presents a novel method for array reconfiguration that is modelled after the widely used Kolakoski Sequence Transform in picture encryption. The suggested approach is assessed in eight different scenarios with 9 x 9 and 5 x 5 PV arrays shaded differently. Its performance is compared against seven established techniques. Due to its intelligent reconfiguration aimed at minimizing shade dispersion, the suggested approach consistently outperforms alternative methods. It results in substantial improvements in GMP, enhancing it by 32.79%, 14.98%, 10.15%, and 4.13% for 9 x 9 arrays, and 37.10%, 14.36%, and 9.88% for 5 x 5 arrays across diverse conditions. Furthermore, this study comprehensively investigates three separate Artificial Neural Network algorithms, specifically the Levenberg-Marquardt (LMB), Scaled Conjugate Gradient, and bayesian regularization algorithms for MPPT. A Levenberg-Marquardt Backpropagation-based MPPT controller for a 250Wp standalone PV system is used to validate the effectiveness of the recommended configuration. This integrated approach, which combines reconfiguration and LMB-based MPPT utilizes only two sensors regardless of array size. It achieves accelerated convergence tracking within a short 0.13 s, displaying minimal steady-state oscillations.

关键词： Artificial neural network bayesian regularization algorithm Global power maximization Levenberg-marquardt backpropagation Maximum power point tracking

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Systematic evaluation of pulsed laser parameters effect on temperature distribution in dissimilar laser welding: A numerical simulation and artificial neural network

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OPTICS AND LASER TECHNOLOGY 2023年第1期163卷

作者： Sun, Chuan Dehkordi, Mohammad Hossein Razavi Kholoud, Mohammad Javad Azimy, Hamidreza Li, Z. Huanggang Normal Univ Sch Electromech & Intelligent Mfg Huanggang Peoples R China Huanggang Normal Univ Hubei Zhongke Res Inst Ind Technol Huanggang Peoples R China Islamic Azad Univ Dept Mech Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Najafabad Branch Aerosp & Energy Convers Res Ctr Najafabad Iran Opole Univ Technol Fac Mech Engn PL-45758 Opole Poland

The heat transfer mechanism and temperature distribution in laser welding applications have a great impact on the quality of the weld bead geometry, mechanical properties and the resultant microstructure characterizations of the welding process. In this study, the effects of pulsed laser welding parameters including the frequency and pulse width on the melt velocity field and temperature distribution in dissimilar laser welding of stainless steel 420 (S.S 420) and stainless steel 304 (S.S 304) was investigated. A comprehensive comparison was conducted through the numerical simulation and artificial neural network (ANN). The results of numerical simulation indicated that buoyancy force and Marangoni stress are the most important factors in the formation of the flow of liquid metal. Also, increasing the pulse width from 8 to 12 ms due to increasing the pulse energy, the temperature in the center of the melt pool increased about 250 C-o. This leads to increasing the convective heat transfer in the molten pool and heat affected zone (HAZ). The temperature difference at a distance of 1 mm from the beam center at both metals at a frequency of 15 and 20 Hz is bout 58 and 75 C, respectively. Furthermore, reducing the frequency to 5 Hz, due to diminishment of thermal energy absorption time, has clearly decreased the weld penetration depth in the workpiece. According to the ANN results, increasing both pulse duration and frequency has the significant effect on increasing melting ratio from 0.4 to 0.8 compared to the other input parameters. The ANN results confirmed that under the same input conditions, because of the differences in thermal conductivity coefficient, absorption coefficient and melting point of the two pieces, S.S 304 has experienced higher temperatures about 10% more than S.S 420. Also, among the 13 back propagation learning algorithms, the bayesian regularization algorithm had the best performance. Among the number of different neurons in the hidden layer, com

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LEVERAGING ON INTELLIGENT COMPUTING NEURO-STRUCTURES FOR POPULATION DYNAMICS OF FINANCIAL BUBBLE MODEL

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SINGAPORE ECONOMIC REVIEW 2025年第0期

作者： Syed, Farwah Ali Fang, Kwo-ting Kiani, Adiqa Kausar Shoaib, Muhammad Raja, Muhammad Asif Zahoor Natl Yunlin Univ Sci & Technol Dept Informat Management 123 Univ RdSect 3 Touliu 64002 Taiwan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Taiwan Yuan Ze Univ AI Ctr Taoyuan 320 Taiwan

This study aims to exploit the Artificial intelligence (AI)-based computing paradigm to analyze the economic system to define the price movements of unsustainable expansion, rapid collapse, and eventual equilibrium that characterize financial bubbles represented with differential equations to portray the role of societal contagion and group mentality from a behavioral viewpoint with the market population classified as bull, i.e., optimistic, neutrals, bear, i.e., pessimistic, and quitter categories. The concept of the financial bubble is characterized as an unexpected rise in prices that is rapidly followed by a shrill decline and retrospectively appears as a consequence of such uncertainty in price value. AI-based applications facilitate financial analysts with innovative computational paradigms for gaining deep insights, improving predictive accuracy, and creating sustained vigorous risk management stratagems for the financial bubble framework and solving with supervised nonlinear autoregressive exogenous networks with optimized bayesian regularization algorithm to accomplish the reasonable predictive accuracy and malleability for the solution of financial bubble behavioral dynamics. The Adams numerical solver accomplishes the acquisition of synthetic data for the execution of a multi-layer structure of exogenous networks to solve for financial bubble parameters termed as contagion rate of optimistic behavior, bearish behavior, pessimist's average time of staying in the bearish group, the pessimist's population effect on the autonomous supply and the rate of optimists conversion to pessimists group while assuming other parameters values to be fixed for demand and supply functions. A consistent overlap between proposed results and synthetic numerical values of a financial bubble model is indicated by negligible error value that verifies the exogenous network's effectiveness and is verified by the enclosure of several evaluation measures of the precision and efficie

关键词： Dynamic nonlinear systems econometrics financial bubble neural networks Adams numerical solver bayesian regularization algorithm autoregressive systems

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Gas path parameter prediction of aero-engine based on an autoregressive discrete convolution sum process neural network

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CHAOS SOLITONS & FRACTALS 2022年第0期154卷 111627-111627页

作者： Cui, Zhiquan Yan, Zhiqi Zhao, Minghang Zhong, Shisheng Harbin Inst Technol Weihai Sch Automot Engn 2 Wenhuaxi Rd Weihai 264209 Peoples R China Harbin Inst Technol Sch Mechatron Engn 92 West Dazhi St Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Weihai Sch Ocean Engn 2 Wenhuaxi Rd Weihai 264209 Peoples R China

In order to improve the approximation ability of neural network to functional and improve the prediction accuracy of time series data, this paper proposes an autoregressive discrete convolution sum process neural network prediction model and applies it to the prediction of aero-engine gas path performance parameters. Coupling threshold wavelet de-noising method is applied to the preprocessing of engine gas path parameters, which can effectively remove the noise in the time series data. Process neurons are applied to artificial neural networks to expand the computational functions of artificial neurons. The process neuron can not only realize the spatial aggregation operation of discrete input data and the connection weight, but also realize the time aggregation operation of the product integral of the continuous input function and the connection weight function. The output of discrete time series is affected by the current input/output, and also by the historical input/output value. Therefore, an autoregressive feedback link is added to the network topology, and the value of the network's output node is used as the input to adjust the network connection weight. The convolution sum operation of discrete time series data can realize the time accumulation effect, so the discrete convolution sum operator is used to replace the integral operator to realize the time aggregation function of the process neuron. Since the integral operation of the continuous function is avoided, the weight training process of the process neural network is effectively simplified, and the accuracy loss in the continuous-time function fitting process of discrete input samples is effectively avoided. bayesian regularization network weight learning algorithm is used to solve the problems of slow convergence speed and easy to fall into local optimum of back propagation learning algorithm, and improve the generalization ability of neural network. It can be seen from the prediction simulation result

关键词： Autoregressive Discrete convolution sum bayesian regularization algorithm Process neural network Gas path parameter

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Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network

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SOFT COMPUTING 2020年第11期24卷 7851-7860页

作者： Cao, Jiasheng Wang, Jinghan Univ Sci & Technol China Hefei Peoples R China IIT Chicago IL 60616 USA

In order to establish an accurate effective stock forecasting model, the principal component analysis (PCA) was first used to analyze the main financial index data of some listed companies and the comprehensive score of evaluation index was obtained in this study. Then, the financial indicator data and the transaction indicator data were simultaneously used as the input variables of the stock price prediction research, three back propagation (BP) neural network algorithms were used for experiment, and its prediction situation was compared. Results show that the BP neural network based on bayesian regularization algorithm has the highest prediction accuracy and can avoid over-fitting phenomenon in the training process of the model;the error between the predicted value and the actual value is small. Finally, this study constructed a stock price prediction study based on PCA and BP neural network algorithm as well as an investment stock selection strategy based on traditional stock selection analysis method. As a result, the proposed model is proved to be effective.

关键词： Stock price forecasting Investment guidance Principal component analysis bayesian regularization algorithm BP neural network

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