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Prediction of stock market index based on ISSA-BP neural network

EXPERT SYSTEMS WITH APPLICATIONS 2022年 204卷

作者： Liu, Xin Guo, Junhong Wang, Hua Zhang, Fan Shandong Technol & Business Univ Sch Stat Yantai 264005 Peoples R China Ludong Univ Sch Informat & Electe Engn Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Shandong Future Intelligent Financial Engn Lab Yantai 264005 Peoples R China

Stock market index forecasting is a very tempting topic. Appropriate analysis of such a topic will provide valuable insights for investors, traders and policymakers in the appealing stock markets. In this article, an improved sparrow search algorithm is proposed to optimize the initial weight and threshold of BPNN, and is applied to the forecasting of stock market indices. The contributions of this article are mainly in the following aspects: (1) Use the sine chaos model to initialize the population;(2) In the position update formula of the discoverer, the global optimal solution of the previous generation is added, and at the same time, adaptive weights are added to coordinate the capabilities of local mining and global exploration. (3) Combining the Gaussian mutation operator and the reverse learning strategy, perturbation mutation is carried out at the optimal position to generate new solutions. (4) Use the improved sparrow algorithm (ISSA) to optimize the initial weight and threshold of BP neural network. We evaluate the performance of the proposed model on four datasets, which are SSE, SZSE, SP500 and DJI. Two types of models are used to compared with the proposed mode. One of them is the use of swarm intelligence optimization algorithms to optimize BPNN, including GA-BP, PSO-BP, ACO-BP, GWO-BP, CS-BP and SSA-BP. The others are some deep learning models. The experimental results show that the three strategies proposed in this paper greatly improve the optimization ability of the sparrow search algorithm. The ISSA-BP model achieve great success in the short-term prediction of stock prices, which can help investors predict market trends and find the right time to trade. In addition, for policymakers, the rationality of policies can be assessed through market trends, so as to better promote the development of the stock market.

关键词： Stock market Sparrow search algorithm BP neural network optimization algorithm

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Optimizing relative root-zone water depletion thresholds to maximize yield and water productivity of winter wheat using AquaCrop

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AGRICULTURAL WATER MANAGEMENT 2023年第1期286卷

作者： Zhang, Ting Zuo, Qiang Ma, Ning Shi, Jianchu Fan, Yuchuan Wu, Xun Wang, Lichun Xue, Xuzhang Ben -Gal, Alon Minist Educ Key Lab Plant Soil Interact Beijing 100193 Peoples R China Minist Agr Key Lab Arable Land Conservat North China Beijing 100193 Peoples R China China Agr Univ Coll Land Sci & Technol Minist Educ Beijing 100193 Peoples R China Pingliang Acad Agr Sci Pingliang Gansu Peoples R China Univ Florida Everglades Res & Educ Ctr 3200 Canal St S Belle Glade FL 33430 USA Natl Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China Gilat Res Ctr Agr Res Org Volcani Inst Soil Water & Environm Sci Mobile Post Negev IL-85280 Ramat Gan Israel China Agr Univ Coll Land Sci & Technol Beijing 100193 Peoples R China

Determination of relative root-zone water depletion (RRWD) thresholds to trigger irrigation is crucial to create optimal irrigation schedules targeting maximum yield and/or water productivity with limited water supply for a crop. In this study, a numerical procedure to determine RRWD thresholds was developed through coupling AquaCrop software with genetic-simplex algorithms. Using a two-year field lysimetric experiment for winter wheat conducted in the North China Plain (NCP), AquaCrop adequately simulated canopy cover, final above-ground biomass, grain yield, seasonal evapotranspiration, and soil water storage, with the normalized root mean squared error (NRMSE) smaller than 15 % and determination coefficient (R2) larger than 0.84. The global op-timum range of RRWD thresholds was preliminarily determined using the genetic algorithm, and subsequently final RRWD thresholds were optimized by fine tuning using the simplex algorithm. The RRWD threshold com-binations (composed of the RRWD thresholds to trigger different sequential irrigation events) for varying number of irrigation events (i.e.1-4) were optimized based on 39 years of historical meteorological data, and the effects of climate change on the optimal crop yield (Ya, opt), water productivity (WPopt), and the combinations of optimized RRWD threshold (RRWDopt) were investigated. The results indicated that both Ya , opt and WPopt generally increased with time showing a tendency of gradually elevated annual CO2 concentration and seasonal average effective temperature. Irrespective of the number of irrigation events during the winter wheat growing season, the differences of RRWDopt for different combinations of irrigation sequence and event in the same kind of hydrological year were relatively small, with a coefficient of variation consistently less than 23 % and a mean of 8 %. When combinations of mean RRWDopt were applied into AquaCrop to trigger irrigation for winter wheat in various hydrological years, the sim

关键词： Crop model optimization algorithm Irrigation scheduling Evapotranspiration Climate change

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Jaya-ICSM: A rapid inverse method driven by monitoring data for concrete-faced rockfill dams static displacement simulation

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ADVANCED ENGINEERING INFORMATICS 2023年第1期57卷

作者： Li, Yichuan Pang, Rui Xu, Bin Zhou, Yang Dalian Univ Technol Fac Infrastructure Engn Sch Hydraul Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Dalian Univ Technol DUT Sch Civil Engn 2 Linggong RdHigh Tech Zone Dalian 116024 Peoples R China Dalian Univ Technol DUT State Key Lab Coastal & Offshore Engn 2 Linggong RdHigh Tech Zone Dalian 116024 Peoples R China

This paper presents a rapid static parameter inverse method for concrete-faced rockfill dams based on displacement data using an improved cloud surrogate model and the Jaya optimization algorithm. The improved cloud surrogate model (ICSM), which evolved from the uncertainty cloud theory, is used rapidly to establish a nonlinear relationship between static parameters and displacement. The Jaya optimization algorithm, which is proficient in recognizing the extreme value of the objective function, is selected for parameter identification. To verify the performance of the proposed method, an inverse analysis of two numerical models and a real engineering project is considered, and a comparative study with the BP neural network as the surrogate model is conducted. Study results demonstrate that ICSM has an extremely high prediction accuracy, with a mean relative error that is always under 1 %. The prediction accuracy does not increase too much even under complex nonlinear conditions or when dealing with sparse training data. Moreover, Jaya-ICSM maintains high computational efficiency throughout the entire inverse analysis, saving more than 98 % of the time compared to another inverse method and proving to be much better than direct inversion based on the finite element method. This enables real-time inverse analysis and state feedback of complex engineering. The results also illustrate that the Jaya-ICSM is promising in parameter recognition and superior in supporting the displacement evaluation of earth-rock dams. Therefore, the proposed method can be an effective tool for engineers to evaluate the safety of earth-rockfill dams and ensure their long-term stability.

关键词： Inverse analysis Concrete -faced rockfill dam Cloud surrogate model optimization algorithm Displacement simulation

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DSGWO: An improved grey wolf optimizer with diversity enhanced strategy based on group-stage competition and balance mechanisms

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Jiang, Jianhua Zhao, Ziying Liu, Yutong Li, Weihua Wang, Huan Jilin Univ Finance & Econ Jilin Prov Key Lab Fintech Changchun 130117 Peoples R China Jilin Agr Univ Res Ctr Corpus Applicat Changchun 130118 Peoples R China Auckland Univ Technol Sch Engn Comp & Math Sci 55 Wellesley St E Auckland 1010 New Zealand

Grey wolf optimizer (GWO) is a popular algorithm in the field of meta-heuristics with fast convergence speed and strong exploitation ability for solving different types of optimization problems. However, there are two problems presented in the optimization process of GWO. Problem (1): GWO algorithm uses three leading wolves to direct the population for search, resulting in poor population diversity. Problem (2): Individuals selected in the optimization process of GWO have the current optimal fitness value, resulting in poor global search capabilities of the algorithm, and the population may tend to converge prematurely and fall into local optimum. In order to solve the above problems, this paper proposes the Diversity enhanced Strategy based Grey Wolf Optimizer (DSGWO), which combines two mechanisms to improve the performance of the GWO algorithm, including the group-stage competition mechanism and the exploration-exploitation balance mechanism. The experimental results are evaluated by using IEEE CEC 2014 benchmark functions and two engineering problems. The Wilcoxon rank-sum test is also conducted to measure the performance of DSGWO. The results demonstrate that the DSGWO algorithm can efficiently obtain the optimal solution of the dataset used in this paper, preserve more population diversity, and enhance the global search capability. In addition, through the application of two engineering problems, it is verified that DSGWO is suitable for solving engineering design problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Grey wolf optimizer GWO Continuous optimization problem

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A Modified Newton-Raphson Search for the Model-adaptive Identification of Delays

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IFAC Proceedings Volumes 1979年第8期12卷 1279-1286页

作者： H. Burkhardt H. Moll Institut für Mess- und Regelungstechnik University of Karlsruhe D-7500 Karlsruhe F.R.G.

The paper presents a new, modified Newton-Raphson technique for the fast, model-adaptive identification of delays between two unknown stochastic or deterministic signals. The method combines properties of the gradient, as well as the Newton-Raphson technique. In contrast to the linear convergence of gradient techniques, it has in this special application the same cubic convergence rate as the Newton-Raphson method combined, however, with the larger range of stability of the gradient method. Further properties are a nearly uniform settling time, independent of the bandwidth and the amplitudes of the measured signals, and under certain assumptions a monotone decreasing error. The algorithm leads straightforwardly to a rather simple, discrete hardware implementation, adjusting a parametric model. Possible applications are in the field of velocity and distance measurement. Extensive simulations for the proposed scheme together with results of known methods are presented.

关键词： Identification Newton-Raphson correlation optimization algorithm stochastic algorithm speed measurement distance measurement tracking system

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Comparison between two different efficiency improvement approaches for an obsolete small-size multi-generation plant

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Energy Procedia 2018年 148卷 583-590页

作者： Piero Danti Sandro Magnani Giovanni Cauchi Yanmar R&D Europe Firenze 50125 Italy

In the latest years, consumer’s needs of reducing energy cost raised a general interest on plants upgrade and optimization; currently there are many approaches to deal with this tendency. Most intuitive way of proceeding is embodied by a revamping solution: often, above all in Italian landscape, small-medium size energy plants run outdated equipment whose performance have considerable room for improvement. Because of that, during last 15 years, many Energy Service Companies (ESCos) have been burgeoning providing a broad range of energy solutions. One of these solutions is represented by trigeneration systems installation, combining CHP and absorption chiller; by means of this a decrease of energy supply costs and CO 2 emissions is achieved. Another philosophy to face the problem is the optimization of generators functioning using ad hoc control logics. This analysis aims to draw a comparison, from an economical point of view, between two different optimization strategies applied to the same small size energy plant where a cogeneration system is installed: the former strategy will focus on plant upgrade, substituting obsolete devices with higher performance ones, the latter will solely lean on an EMS running an optimization algorithm. Results will be presented and analysed in terms of daily costs.

关键词： CHP EHP EMS optimization algorithm

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ORIENTATION STUDIES FOR SINGLE-AXIS CONCENTRATING COLLECTORS

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JOURNAL OF ENERGY 1979年第3期3卷 151-155页

作者： JETER, S CRAIG, JI GREMS, EG GEORGIA INST TECHNOL SCH AEROSP ENGNATLANTAGA 30332

The useful heat gain of a linear single-axis concentrating solar collector is significantly affected bY the orientation of the collector. The temporal distribution of the heat output, both diurnally and annually, and the integrated energy output varY with the direction of the collector axis. For a typical insolation sequence, a north to south orientation yields greater annual heat gain while an east to west orientation gives a flatter annual distribution. Selection of the best collector orientation depends on this distribution, the load profile, and the thermal capacitance of the system. The first two factors are considered in the definition of an economic criterion function which is then used to identify the optimum distribution of collectors among all possible orientations.

关键词： Transmittance Solar System Heat Flow Material Properties Solar Energy Systems Newton's Method optimization algorithm Thermal Performance Gradient Method Solar Collectors

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A 3-D Kinematic Model For The Biomechanical Analysis Of The Lower Limb While Cycling

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International Journal of Modelling and Simulation 1990年第2期10卷 36-42页

作者： R. Radharamanan H. Shen Department of Mechanical and Industrial Engineering Marquette University Milwaukee WI 53233 Graduate Student Department of Mechanical Engineering The Pennsylvania State University University Park PA 16802

In this paper, a ten-joint 3-D kinematic model has been developed by carefully *** the anatomy of the lower limb during bicycling for understandmg and analyzmg the complex motions of the joints. An optimization algorithm has also been developed and used to compute the Joint vanables and find the numerical solution. In order to simulate the indeterminate kinematic system, random vectors of multinormal distribution have been generated and statistical means and variances have been obtained from these random vector samples. The validity of the developed model has been checked by comparing the results with that of *** measurements. The model developed has good desirability to descnbe the hmb and IS more accurate than 2-D models to predict the motion of lower extremities.

关键词： Kinematic Model Biomechanical Analysis Lower Limb Multinormal Distribution optimization algorithm Bicycling

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Parameter extraction of photovoltaic cell and module: Analysis and discussion of various combinations and test cases

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2020年 40卷

作者： Oulcaid, Mostapha El Fadil, Hassan Ammeh, Leila Yahya, Abdelhafid Giri, Fouad Ibn Tofail Univ LGS Lab Kenitra 14000 Morocco Univ Caen Lab Automat Caen Bd Marechal JuinBP 8156 F-14032 Caen France

A reliable model for photovoltaic (PV) cell/panel is of great interest;it helps to simulate and better understand the behavior of PV systems. Consequently, better control and optimization of the system can be achieved. The most used models of PV cell/panel in literature are the one-diode (ODM) and double-diode (DDM) models. Their accuracy strongly depends on their parameters which must be obtained carefully. optimization-based approaches are widely adopted for this purpose. In this context, this paper presents two main references studies: The first one focuses on optimization methods and their three basic elements: model, objective function and algorithm. The study highlights the improvement that can be made to the accuracy of the final I-V curve and to the CPU-time required for the extraction by choosing the right combination of the three elements. This is done through the analysis of more than 50 results obtained by different combinations of algorithms, objective functions and models applied to benchmark PV cells and panels widely used in literature. The best combinations are identified and recommended. The second is a detailed comparison and classification of 47 I-V curve estimation methods based on three different approaches: optimization techniques, analytical methods and explicit models of PV cells/panels.

关键词： Photovoltaic cell model optimization algorithm Parameter extraction Analytical approach Explicit models

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Optimized inversion method for thermal parameters of concrete dam under the insulated condition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期126卷

作者： Bo, Chen Qingyi, Wang Weinan, Chen Hao, Gu Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Water Conservancy & Hydroelect Power Co Ltd Zhejiang Design Inst Hangzhou 310000 Peoples R China

It is crucial to study how thermal characteristics of concrete dam change over time, especially in cold regions, in order to guarantee long-term safety of the engineering projects. In this manuscript, an inverse analysis method by coupling the numerical simulation model and dam safety monitoring data, is proposed to solve the problem that true thermal parameters of the whole structure cannot be obtained by laboratory experiments or field tests of single points. Moreover, a numerical equivalence method of the dam and its insulation layer is introduced, in order to overcome the problem that the thickness of the insulation layer is much smaller than that of the concrete dam, resulting in low accuracy of the simulation model. Thirdly, the particle swarm optimization is introduced, in order to solve the ill-posed problems occurring during the solution process of multi-parameter inverse analysis. Considering the problem that the original particle swarm algorithm is easy to fall into local optimum and stuck edge, this study improves particles velocity and inertia weights. Benchmark function tests are applied to show the performance compared to several typical optimized algorithms. Numerical simulation and engineering verifi-cation are used to demonstrate the rationality and feasibility of the proposed method. The cases show that the residual box diagram of two typical measuring points calculated by inversion value is smaller than the median line and mean point calculated by design value. Moreover, the change law of the temperature field of the finite element calculation using the inversion values is closer to the actual measured value than that of the design value. The results indicate that the multi-parameter inverse method is feasible in insulated dam engineering. In addition, the result of the improved PSO optimization converges faster and has a higher fitness value than that of the original algorithm. It suggests that the improved PSO algorithm for thermal parameters i

关键词： Cold region Concrete dam Dam insulation Equivalent heat release coefficient optimization algorithm

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