Solving nonlinear integral equations with non-separable kernel via a high-order iterative process

[EN] In this work we focus on location and approximation of a solution of nonlinear integral equations of Hammerstein-type when the kernel is non-separable through a high order iterative process. For this purpose, we approximate the non-separable kernel by means of a separable kernel and then, we perform a complete study about the convergence criteria for the approximated solution obtained to the solution of our first problem. Different examples have been tested in order to apply our theoretical results.This research was partially supported by Ministerio de Economia y Competitividad under grant PGC2018-095896-B-C21-C22 and by the project EEQ/2018/000720 under Science and Engineering Research Board.Hernández-Verón, MA.; Yadav, S.; Martínez Molada, E.; Singh, S. (2021). Solving nonlinear integral equations with non-separable kernel via a high-order iterative process. Applied Mathematics and Computation. 409:1-12. https://doi.org/10.1016/j.amc.2021.126385S11240

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.The research of Tinkle Chugh was funded by the COMAS Doctoral Program (at the University of Jyväskylä) and FiDiPro Project DeCoMo (funded by Tekes, the Finnish Funding Agency for Innovation), and the research of Dr. Karthik Sindhya was funded by SIMPRO project funded by Tekes as well as DeCoMo

Contributions to fuzzy polynomial techniques for stability analysis and control

The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees. The contributions of the thesis are: ¿ Improved domain of attraction estimation of nonlinear systems for both continuous-time and discrete-time cases. An iterative methodology based on invariant-set results is presented for obtaining polynomial boundaries of such domain of attraction. ¿ Extension of the above problem to the case with bounded persistent disturbances acting. Different characterizations of inescapable sets with polynomial boundaries are determined. ¿ State estimation: extension of the previous results in literature to the case of fuzzy observers with polynomial gains, guaranteeing stability of the estimation error and inescapability in a subset of the zone where the model is valid. ¿ Proposal of a polynomial Lyapunov function with discrete delay in order to improve some polynomial control designs from literature. Preliminary extension to the fuzzy polynomial case. Last chapters present a preliminary experimental work in order to check and validate the theoretical results on real platforms in the future.Pitarch Pérez, JL. (2013). Contributions to fuzzy polynomial techniques for stability analysis and control [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34773TESI

Piecewise-Takagi-Sugeno asymptotically exact estimation of the domain of attraction of nonlinear systems

[EN] This report generalises recent results on stability analysis and estimation of the domain of attraction of nonlinear systems via exact piecewise affine Takagi Sugeno models. Algorithms in the form of linear matrix inequalities are proposed that produce progressively better estimates which are proved to asymptotically render the actual domain of attraction; regions already proven to belong to such domain of attraction can be removed and the estimate can contain significant portions of the modelling region boundary; in this way, level-set approaches in prior literature can be significantly improved. Illustrative examples and comparisons are provided. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.The authors gratefully acknowledge the support of the following institutions: Project Ciencia Basica SEP-CONACYT CB-168406, the CONACyT/COECYT Sonora scholarship 383252, project DPI2016-81002-R (Spanish government, MINECO), and the scholarship GRISOLIA/2014/006 from Generalitat Valenciana (regional government).Gonzalez-German, IT.; Sala, A.; Bernal Reza, MÁ.; Robles-Ruiz, R. (2017). Piecewise-Takagi-Sugeno asymptotically exact estimation of the domain of attraction of nonlinear systems. Journal of the Franklin Institute. 354(3):1514-1541. https://doi.org/10.1016/j.jfranklin.2016.11.033S15141541354

Differential quadrature method (DQM) and Boubaker Polynomials Expansion Scheme (BPES) for efficient computation of the eigenvalues of fourth-order Sturm-Liouville problems

The differential quadrature method (DQM) and the Boubaker Polynomials Expansion Scheme (BPES) are applied in order to compute the eigenvalues of some regular fourth-order Sturm-Liouville problems. Generally, these problems include fourth-order ordinary differential equations together with four boundary conditions which are specified at two boundary points. These problems concern mainly applied-physics models like the steady-state Euler-Bernoulli beam equation and mechanicals non-linear systems identification. The approach of directly substituting the boundary conditions into the discrete governing equations is used in order to implement these boundary conditions within DQM calculations. It is demonstrated through numerical examples that accurate results for the first kth eigenvalues of the problem, where k= 1,. 2,. 3,. .... , can be obtained by using minimally 2(k+. 4) mesh points in the computational domain. The results of this work are then compared with some relevant studies. © 2011 Elsevier Inc

Probabilistic analysis of random nonlinear oscillators subject to small perturbations via probability density functions: Theory and computing

[EN] We study a class of single-degree-of-freedom oscillators whose restoring function is affected by small nonlinearities and excited by stationary Gaussian stochastic processes. We obtain, via the stochastic perturbation technique, approximations of the main statistics of the steady state, which is a random variable, including the first moments, and the correlation and power spectral functions. Additionally, we combine this key information with the principle of maximum entropy to construct approximations of the probability density function of the steady state. We include two numerical examples where the advantages and limitations of the stochastic perturbation method are discussed with regard to certain general properties that must be preservedThis work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI), and Fondo Europeo de Desarrollo Regional (FEDER UE) Grant PID2020-115270GB-I00. The authors express their deepest thanks and respect to the reviewers for their valuable commentsCortés, J.; López-Navarro, E.; Romero, J.; Roselló, M. (2021). Probabilistic analysis of random nonlinear oscillators subject to small perturbations via probability density functions: Theory and computing. European Physical Journal Plus. 136(7):1-23. https://doi.org/10.1140/epjp/s13360-021-01672-wS1231367W.L. Oberkampf, S.M. De Land, B.M. Rutherford, K.V. Diegert, K.F. Alvin, Error and uncertainty in modeling and simulation. Reliab. Eng. Syst. Saf. 75, 333–357 (2002)T. Soong, Random Differential Equations in Science and Engineering, vol. 103 (Academic Press, New York, 1973)Kloeden, P., Platen, E.: Numerical Solution of Stochastic Differential Equations, Ser. Stochastic Modelling and Applied Probability, vol. 23. Springer, Berlin Heidelberg (1992)J.L. Bogdanoff, J.E. Goldberg, M. Bernard, Response of a simple structure to a random earthquake-type disturbance. Bull. Seismol. Soc. Am. 51, 293–310 (1961)L. Su, G. Ahmadi, Earthquake response of linear continuous structures by the method of evolutionary spectra. Eng. Struct. 10, 47–56 (1988)X. Jin, Y. Tian, Y. Wang, Z. Huang, Explicit expression of stationary response probability density for nonlinear stochastic systems. Acta Mech. 232, 2101–2114 (2021)D. Lobo, T. Ritto, D. Castello, E. Cataldo, Dynamics of a Duffing oscillator with the stiffness modeled as a stochastic process. Int. J. Non-Linear Mech. 116, 273–280 (2019)Y. Lin, G. Cai, Probabilistic Structural Dynamics: Advanced Theory and Applications (McGraw-Hill, Cambridge, 1995)C. To, Nonlinear Random Vibration: Analytical Techniques and Applications (Swets & Zeitlinger, New York, 2000)M. Kaminski, The Stochastic Perturbation Method for Computational Mechanics (Wiley, New York, 2013)J.J. Stoker, Nonlinear Vibrations (Wiley (Interscience), New York, 1950)N. McLachlan, Laplace Transforms and Their Applications to Differential Equations, vol. 103 (Dover Publ. INc., New York, 2014)R.F. Steidel, An Introduction to Mechanical Vibrations (Wiley, New York, 1989)G. Casella, R. Berger, Statistical Inference (Cengage Learning, New Delhi, 2007)H.V. Storch, F.W. Zwiers, Statistical Analysis in Climate Research (Cambridge University Press, Cambridge, 2001)J.V. Michalowicz, J.M. Nichols, F. Bucholtz, Handbook of Differential Entropy (CRC Press, Boca Raton, 2018)H. Banks, H. Shuhua, W. Clayton Thompson, Modelling and Inverse Problems in the Presence of Uncertainty (Ser. Monographs and Research Notes in Mathematics. CRC Press, Boca Raton, 2001)Garg, V.K., Wang, Y.-C.: 1 - signal types, properties, and processes. In: Chen, W.-K. (ed.) The Electrical Engineering Handboo

The Magnus expansion and some of its applications

Approximate resolution of linear systems of differential equations with varying coefficients is a recurrent problem shared by a number of scientific and engineering areas, ranging from Quantum Mechanics to Control Theory. When formulated in operator or matrix form, the Magnus expansion furnishes an elegant setting to built up approximate exponential representations of the solution of the system. It provides a power series expansion for the corresponding exponent and is sometimes referred to as Time-Dependent Exponential Perturbation Theory. Every Magnus approximant corresponds in Perturbation Theory to a partial re-summation of infinite terms with the important additional property of preserving at any order certain symmetries of the exact solution. The goal of this review is threefold. First, to collect a number of developments scattered through half a century of scientific literature on Magnus expansion. They concern the methods for the generation of terms in the expansion, estimates of the radius of convergence of the series, generalizations and related non-perturbative expansions. Second, to provide a bridge with its implementation as generator of especial purpose numerical integration methods, a field of intense activity during the last decade. Third, to illustrate with examples the kind of results one can expect from Magnus expansion in comparison with those from both perturbative schemes and standard numerical integrators. We buttress this issue with a revision of the wide range of physical applications found by Magnus expansion in the literature.Comment: Report on the Magnus expansion for differential equations and its applications to several physical problem