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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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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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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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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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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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Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

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WATER RESOURCES MANAGEMENT 2010年第9期24卷 1845-1865页

作者： Mohanty, Sheelabhadra Jha, Madan K. Kumar, Ashwani Sudheer, K. P. Directorate Water Management Bhubaneswar 751023 Orissa India Indian Inst Technol Kharagpur AgFE Dept Kharagpur 721302 W Bengal India Indian Inst Technol Madras Dept Civil Engn Madras Tamil Nadu India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg-Marquardt (LM) algorithm and bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.

关键词： Artificial neural network Groundwater level prediction Backpropagation GDX algorithm Lavenberg-Marquardt algorithm bayesian regularization algorithm River island

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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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Intelligent Model of Microwave Filter Using PDGS with Defected Rectangles

Intelligent Model of Microwave Filter Using PDGS with Defect...

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Jin Taobin Jin Jie Yao Ruipu Li Kejia Zhang Yizhen Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Tianjin Univ Sch Electron Inf Eng Tianjin Peoples R China

ISBN: (纸本)9781424437092

Microwave filter is the kind of device which can separate different signals within a certain band of microwave frequency, widely used in microwave communication system. Periodic defected ground structures (PDGS) with defected rectangles have excellent filter properties when periodic unit amounts and structure sizes meet appropriate conditions. In this paper, intelligent model of PDGS with defected rectangles is developed for the first time on the basis of FDTD analysis. Periodic unit amounts, the structure sizes of PDGS with defected rectangles and the frequency are defined as the input samples of artificial neural network (ANN), transmission coefficient (S21) are defined as the output samples. Transmission coefficient of PDGS at any arbitrary periodic unit amounts, any arbitrary structure sizes and any arbitrary frequency within training values range can be obtained quickly from intelligent model after the ANN has been successfully trained with the bayesian regularization algorithm. Finally, intelligent model has been approved by FDTD results. It is also showed that intelligent model is very effective, which will provide powerful approach for the precise analysis and quick design of microwave filter using PDGS with defected rectangles.

关键词： microwave filter periodic unit amounts bayesian regularization algorithm transmission coefficient

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Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection

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Petroleum Exploration and Development 2020年第2期47卷 383-392页

作者： NEGASH Berihun Mamo YAW Atta Dennis University Teknologi PETRONAS Petroleum Engineering Department32610 Seri IskandarPerak Darul RidzuanMalaysia

As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.

关键词： neural networks machine learning attribute extraction bayesian regularization algorithm production forecasting water flooding

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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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