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Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2021年第4期4卷 259-274页

作者： Onyelowe, Kennedy C. Iqbal, Mudassir Jalal, Fazal E. Onyia, Michael E. Onuoha, Ifeanyichukwu C. Michael Okpara Univ Agr Dept Civil Engn Umudike Nigeria Kampala Int Univ Dept Civil & Mech Engn Kampala Uganda Shangai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai Peoples R China Univ Nigeria Fac Engn Dept Civil Engn Nsukka Nigeria Fed Univ Technol Owerri Dept Environm Technol Owerri Nigeria

Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg-Muarquardt Backpropagation (LMBP), Bayesian Programming (BP), and conjugate gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil's strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (w(L)), (plastic limit (w(P)), plasticity index (I-P), optimum moisture content (w(OMC)), clay activity (A(C)), and (maximum dry density (delta(max)). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models' accuracy.

关键词： Soft computing Artificial intelligence Artificial neural network (ANN) Machine learning in geotechnics Back-propagation algorithm Levenberg– muarquardt algorithm Bayesian algorithm conjugate gradient algorithm Sustainable construction materials

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An acceleration algorithm for image reconstruction based on continuous-discrete mapping model

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OPTICAL REVIEW 2000年第2期7卷 132-137页

作者： Yamaya, T Obi, T Yamaguchi, M Ohyama, N Tokyo Inst Technol Imaging Sci & Engn Lab Midori Ku Yokohama Kanagawa 2268503 Japan

In image reconstruction for X-ray computed tomography, images reconstructed by filtered backprojection (FBP) include systematic errors because the FBP method does not take into account some of the properties of the actual imaging system such as the divergence of X-ray beam. One solution to this problem is to use algebraic reconstruction methods, such as generalized analytic reconstruction from discrete samples and natural pixel decomposition. However, in the process of reconstruction using these methods, it is necessary to solve the linear algebraic equations which have a large coefficient matrix. In this paper, we propose a method to accelerate the iteration solving these equations by preconditioning the coefficient matrix using a polynomial function. The results of the computer simulations show the effectiveness of the proposed method.

关键词： computed tomography GARDS NPD continuous-discrete mapping model conjugate gradient algorithm preconditioning acceleration

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An efficient algorithm for large-scale nonlinear programming problems with simple bounds on the variables

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SIAM JOURNAL ON OPTIMIZATION 1998年第2期8卷 532-560页

作者： Pytlak, R Univ London Imperial Coll Sci Technol & Med Ctr Proc Syst Engn London SW7 2BT England

In this paper we present a new conjugate gradient algorithm for nonlinear programming problems with simple bounds on the variables. The method is an extension of our conjugate gradient algorithm described in [R. Pytlak, IMA J. Numer. Anal., 14 (1994), pp. 443-460]. The simple constraints on the variables are treated by a projection. Under mild assumptions the method is globally convergent. The algorithm has the property whereby many constraints can leave or enter an active set of constraints at one iteration. If the strict complementarity condition is satisfied, the active set at a solution is identified in a finite number of iterations. The algorithm has been tested on problems with more than 10,000 variables.

关键词： conjugate gradient algorithm box constraints projection large-scale problems

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Classification of GPS Satellites Using Improved Back Propagation Training algorithms

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WIRELESS PERSONAL COMMUNICATIONS 2013年第2期71卷 789-803页

作者： Azami, Hamed Mosavi, Mohammad-Reza Sanei, Saeid Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran Univ Surrey Fac Engn & Phys Sci Dept Comp Guildford GU2 5XH Surrey England

Factor geometric dilution of precision (GDOP) is an indicator that shows the quality of GPS positioning and has often been used for choosing suitable satellite's subset from at least 24 orbited existing satellites. The calculation of GPS GDOP is a time-consuming task which can be done by solving measurement equations with complicated matrix transformation and inversion. In order to decrease this computational burden, in this research the artificial neural network (ANN) has been used. Although the basic back propagation (BP) is the most popular ANN algorithm and can be used in the estimators, detectors or classifiers, it is too slow for practical problems and its performance is not satisfactory in many cases. To overcome this problem, six algorithms, namely, BP with adaptive learning rate and momentum, Fletcher-Reeves conjugate gradient algorithm (CGA), Polak-Ribikre CGA, Powell-Beale CGA, scaled CGA, and resilient BP have been proposed to reduce the convergence time of the basic BP. The simulation results show that resilient BP, compared with other methods, has greater accuracy and calculation time. The resilient BP can improve the classification accuracy from 93.16 to 98.02 % accuracy by using the GPS GDOP measurement data.

关键词： GPSGDOP Classification Artificial neural network Basic back propagation Resilient back propagation conjugate gradient algorithm

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A low-complexity iterative algorithm for multiuser millimeter-wave systems

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ANNALS OF TELECOMMUNICATIONS 2024年第1-2期79卷 101-110页

作者： Mulla, Mustafa Ulusoy, Ali Hakan Rizaner, Ahmet Amca, Hasan Middle East Tech Univ Dept Elect & Elect Engn Northern Cyprus CampusVia Mersin 10 TR-99738 Guzelyurt North Cyprus Turkiye Eastern Mediterranean Univ Sch Comp & Technol Dept Informat Technol Via Mersin 10 TR-99628 Famagusta North Cyprus Turkiye Eastern Mediterranean Univ Dept Elect & Elect Engn Via Mersin 10 TR-99628 Famagusta North Cyprus Turkiye

In this paper, we design a low-complexity multiuser millimeter-wave massive-multiple-input-multiple-output system with the help of a hybrid analog/digital precoding architecture. Hybrid precoding is used to reduce the hardware cost and power consumption of millimeter-wave large-scale antenna systems. In this manner, we proposed a novel approach to solve the well-known zero-forcing algorithm by using an iterative optimization method called the conjugate gradient method. The problem is transformed into an optimization problem, and the complex matrix inverse operation required in the zero-forcing algorithm is eliminated. Hence, the complexity of the zero-forcing algorithm is reduced while the spectral efficiency is maintained at the same level as that of the reference zero-forcing detector. The simulation results demonstrate that the proposed conjugate gradient-based algorithm achieves better performance than competing methods in terms of complexity and spectral efficiency.

关键词： Multiuser Millimeter wave Massive multiple-input-multiple-output Hybrid precoding Iterative optimization conjugate gradient algorithm

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Numerical Estimation of the Inverse Eigenvalue Problem for a Weighted Helmholtz Equation

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JOURNAL OF SCIENTIFIC COMPUTING 2023年第1期96卷 1-31页

作者： Zhang, Zhengfang Gao, Xiangjing Cheng, Xiaoliang Hangzhou Dianzi Univ Coll Sci Hangzhou 310018 Peoples R China Zhejiang Univ Dept Math Hangzhou 310027 Peoples R China

The inverse eigenvalue problem for a weighted Helmholtz equation is investigated. Based on the finite spectral data, the density function is estimated. The inverse problem is formulated as a least squared functional with respect to the density function, with a L(2 )regularity term. The continuity of the eigenpairs with respect to the density is proved. Mathematical properties of the continuous and the discrete optimization problems are established. A conjugate gradient algorithm is proposed. Numerical results for 1D and 2D inverse eigenvalue problem of the weighted Helmholtz equation are presented to illustrate the effectiveness and efficiency of the proposed algorithm.

关键词： Inverse eigenvalue problem Weighted Helmholtz equation conjugate gradient algorithm Finite element method

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FOURIER ACCELERATION OF ITERATIVE PROCESSES IN DISORDERED-SYSTEMS

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JOURNAL OF STATISTICAL PHYSICS 1988年第3-4期52卷 747-773页

作者： BATROUNI, GG HANSEN, A ECOLE NORMALE SUPER PHYS SOLIDES GRPF-75231 PARISFRANCE UNIV COLOGNE INST THEORET PHYSD-5000 COLOGNE 41FED REP GER

Technical details are given on how to use Fourier acceleration with iterative processes such as relaxation and conjugate gradient methods. These methods are often used to solve large linear systems of equations, but become hopelessly slow very rapidly as the size of the set of equations to be solved increases. Fourier acceleration is a method designed to alleviate these problems and result in a very fast algorithm. The method is explained for the Jacobi relaxation and conjugate gradient methods and is applied to two models: the random resistor network and the random central-force network. In the first model, acceleration works very well; in the second, little is gained. We discuss reasons for this. We also include a discussion of stopping criteria.

关键词： conjugate gradient algorithm critical slowing down disordered systems Fourier acceleration relaxation

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Spatiotemporal hierarchical Bayesian modeling: Tropical ocean surface winds

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2001年第454期96卷 382-397页

作者： Wikle, CK Milliff, RF Nychka, D Berliner, LM Univ Missouri Dept Stat Columbia MO 65211 USA Colorado Res Associates Boulder CO 80301 USA Natl Ctr Atmospher Res Geophys Stat Project Boulder CO 80307 USA Ohio State Univ Dept Stat Columbus OH 43210 USA

Spatiotemporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal variability. The complexity of these processes and the large number of observation/prediction locations preclude the use of traditional covariance-based spatiotemporal statistical methods. Alternatively, we focus on conditionally specified (i.e., hierarchical) spatiotemporal models. These methods offer several advantages over traditional approaches. Primarily, physical and dynamical constraints can be easily incorporated into the conditional formulation, so that the series of relatively simple yet physically realistic conditional models leads to a much more complicated spatiotemporal covariance structure than can be specified directly. Furthermore, by making use of the sparse structure inherent in the hierarchical approach, as well as multiresolution (wavelet) bases, the models can be computed with very large datasets. This modeling approach was necessitated by a scientifically meaningful problem in the geosciences. Satellite-derived wind estimates have high spatial resolution but limited global coverage. In contrast, wind fields provided by the major weather centers provide complete coverage but have low spatial resolution. The god is to combine these data in a manner that incorporates the space-time dynamics inherent in the surface wind field. This is an essential task to enable meteorological research, because no complete high-resolution surface wind datasets exist over the world oceans. High-resolution datasets of this type are crucial for improving our understanding of global air-sea interactions affecting climate and tropical disturbances, and for driving large-scale ocean circulation models.

关键词： climate combining information conjugate gradient algorithm dynamical model fractal process Gibbs sampling numerical model ocean model satellite data turbulence wavelets

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A Numerical Method for Cavity Identification in Beams on an Elastic Foundation

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Tsinghua Science and Technology 2007年第5期12卷 540-545页

作者：葛洪海许金余 School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University

An analytical solution for the natural frequencies of a beam containing a cavity on an elastic foundation is presented. Based on the analytical solution, a numerical method for identifying cavities in the foundation is developed. The position and size of the cavities are identified by minimizing an objective function, which is formulated according to the difference between the computed and measured natural frequencies of the system. The conjugate gradient algorithm is adopted for minimizing the objective function. Some numerical examples are presented to demonstrate the applicability of the presented cavity determination method. The results show that the presented method can be used to identify the cavity position and size conveniently and efficiently.

关键词： pavement-subgrade system beam on an elastic foundation cavity identification natural frequency conjugate gradient algorithm

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Fast approximate likelihood evaluation for stable VARFIMA processes

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STATISTICS & PROBABILITY LETTERS 2015年 103卷 160-168页

作者： Pai, Jeffrey Ravishanker, Nalini Univ Manitoba Warren Ctr Actuarial Studies & Res Winnipeg MB R3T 5V4 Canada Univ Connecticut Dept Stat Storrs CT 06269 USA

For VARFIMA models with sub-Gaussian stable errors, we present fast approximate likelihood computation by using a multivariate preconditioned conjugate gradient (MPCG) algorithm, and Monte Carlo integration over unobserved variables. We illustrate our approach on daily average temperatures measured at several US cities. (C) 2015 Elsevier B.V. All rights reserved.

关键词： conjugate gradient algorithm Fast Fourier transform Long memory Preconditioning Sub-Gaussian stable errors

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