基于正则化网络-遗传算法的属性筛选及其在化学模式识别中的应用

采用贝叶斯正则化方法训练，以得到推广性优良的神经网络，并提出启发性的遗传算法。通过灵敏度分析对正则化网络实施剪枝，从而在高维模式中筛选出能代表其分类特性的最小最优属性特征子集。此方法应用于高维留兰香模式的属性筛选与模式分类，效果良好，明显优于其它方法。     

Numerical approximations and regularizations of Riccati equations arising in hyperbolic dynamics with unbounded control operators

This paper provides an approximation theory for numerical computations of the solutions to algebraic Riccati equations arising in hyperbolic, boundary control problems. One of the difficulties in the approximation theory for Riccati equations is that many attractive numerical methods (such as standard finite elements) do not satisfy a uniform stabilizability condition, which is necessary for the stability of the approximate Riccati solutions. To deal with these problems, a regularizationapproximation technique, based on the introduction of special artificial terms to the dynamics of the original model, is proposed. The need for this regularization appears to be a distinct feature of hyperbolic (hyperbolic-like) equations, rather than parabolic (parabolic-like) problems where the smoothing effect of the dynamics is beneficial for the convergence and stability properties of approximate solutions to the associated Riccati equations (see [14]). The ultimate result demonstrates that the regularized, finite-dimensional feedback control yields near optimal performance and that the corresponding Riccati solution satisfies all the desired convergence properties. The general theory is illustrated by an example of a boundary control problem associated with the Kirchoff plate model. Some numerical results are provided for the given example.     

Regularization of a final value problem for a nonlinear biharmonic equation

In this paper, we consider the nonlinear biharmonic equation. The problem is ill‐posed in the sense of Hadamard. To obtain a stable numerical solution, we consider a regularization method. We show rigourously, with error estimates provided, that the corresponding regularized solutions converge to the true solution strongly in uniformly with respect to the space coordinate under some a priori assumptions on the solution.     

Regularization method with two parameters for nonlinear ill-posed problems

This paper is devoted to the regularization of a class of nonlinear ill-posed problems in Banach spaces. The operators involved are multi-valued and the data are assumed to be known approximately. Under the assumption that the original problem is solvable, a strongly convergent approximation procedure is designed by means of the Tikhonov regularization method with two pa- rameters.     

Inverse problems for multi-valued quasi variational inequalities and noncoercive variational inequalities with noisy data

ABSTRACT

We study the inverse problem of identifying a variable parameter in variational and quasi-variational inequalities. We consider a quasi-variational inequality involving a multi-valued monotone map and give a new existence result. We then formulate the inverse problem as an optimization problem and prove its solvability. We also conduct a thorough study of the inverse problem of parameter identification in noncoercive variational inequalities which appear commonly in applied models. We study the inverse problem by posing optimization problems using the output least-squares and the modified output least-squares. Using regularization, penalization, and smoothing, we obtain a single-valued parameter-to-selection map and study its differentiability. We consider optimization problems using the output least-squares and the modified output least-squares for the regularized, penalized and smoothened variational inequality. We give existence results, convergence analysis, and optimality conditions. We provide applications and numerical examples to justify the proposed framework.     

Introducing Alternative Algorithms for the Determination of the Distribution of Relaxation Times

Impedance spectroscopy is a powerful characterization method to evaluate the performance of electrochemical systems. However, overlapping signals in the resulting impedance spectra oftentimes cause misinterpretation of the data. The distribution of relaxation times (DRT) method overcomes this problem by transferring the impedance data from the frequency domain into the time domain, which yields DRT spectra with an increased resolution. Unfortunately, the determination of the DRT is an ill-posed problem, and appropriate mathematical regularizations become inevitable to find suitable solutions. The Tikhonov algorithm is a widespread method for computing DRT data, but it leads to unlikely spectra due to necessary boundaries. Therefore, we introduce the application of three alternative algorithms (Gold, Richardson Lucy, Sparse Spike) for the determination of stable DRT solutions and compare their performances. As the promising Sparse Spike deconvolution has a limited scope when using one single regularization parameter, we furthermore replaced the scalar regularization parameter with a vector. The resulting method is able to calculate well-resolved DRT spectra.     

On the Size of the Plasma Region

AMS (MOS): 35J60, 35B40, 35J67     

The calibration of volatility for option pricing models with jump diffusion processes

This paper is devoted to calibrate smooth local volatility surface under jump-diffusion processes. This calibration problem is posed as an inverse problem: given a finite set of observed European option prices, find a local volatility function such that the theoretical option prices matches the observed ones optimally with respect to a prescribed performance criterion. Firstly, we obtain an Euler-Lagrange equation for the calibration problem using Tikhonov regularization method. Then we solve the Euler–Lagrange equation using an iterative algorithm and obtain the volatility. Finally, numerical experiments show the effectiveness of the proposed method.     

BLOCK-TRIANGULAR PRECONDITIONERS FOR SYSTEMS ARISING FROM EDGE-PRESERVING IMAGE RESTORATION

Signal and image restoration problems are often solved by minimizing a cost function consisting of an l2 data-fidelity term and a regularization term. We consider a class of convex and edge-preserving regularization functions. In specific, half-quadratic regularization as a fixed-point iteration method is usually employed to solve this problem. The main aim of this paper is to solve the above-described signal and image restoration problems with the half-quadratic regularization technique by making use of the Newton method. At each iteration of the Newton method, the Newton equation is a structured system of linear equations of a symmetric positive definite coefficient matrix, and may be efficiently solved by the preconditioned conjugate gradient method accelerated with the modified block SSOR preconditioner. Our experimental results show that the modified block-SSOR preconditioned conjugate gradient method is feasible and effective for further improving the numerical performance of the half-quadratic regularization approach.