Determination of priority among correlated inputs in source identification problems

1992 ◽  
Vol 6 (6) ◽  
pp. 491-502 ◽  
Author(s):  
Jung-Seok Park ◽  
Kwang-Joon Kim
Keyword(s):  
Source Identification ◽  
Identification Problems

scholarly journals The vector optimization and modeling of a technical systems

2018 ◽  
Vol 224 ◽  
pp. 04020
Author(s):  
Leonid B. Matusov
Keyword(s):  
Parameter Space ◽  
Multicriteria Optimization ◽  
Feasible Solution ◽  
Solution Set ◽  
Identification Problems ◽  
Design Variables ◽  
Technical Systems ◽  
The Mathematical Model ◽  
Term Identification

The construction a feasible solution set with a given accuracy is a main problem in multicriteria optimization and modeling. In order to construct the feasible solution set, a method called the Parameter Space Investigation has been created and successfully integrated into various fields of industry, science, and technology. Multicriteria modeling (identification) is a new direction that is of great value in applications. In the most common usage, the term “identification” means construction of the mathematical model of a system and determination of the parameters (design variables) of the model. The construction a feasible solution set with a given accuracy is a common way for solving multicriteria optimization and modeling problems. The issues of the estimation of the Parameter Space Investigation method convergence rate, the approximation of the feasible solution set are described. Besides these, the multicriteria identification problems of mechanical systems are discussed too.

Dynamic programming, pattern recognition and location of faults in complex systems

1966 ◽  
Vol 3 (01) ◽  
pp. 268-271
Author(s):  
Richard Bellman
Keyword(s):  
Complex System ◽  
Functional Equation ◽  
Pattern Recognition ◽  
Dynamic Programming ◽  
Decomposition Technique ◽  
Extended State ◽  
Identification Problems ◽  
State Variable ◽  
Computational Solution

In a previous paper devoted to an application of dynamic programming to pattern recognition [1], we pointed out that some identification problems could be regarded as generalized trajectory processes. The functional equation technique [2] could then be employed to obtain an analytic formulation of the determination of optimal search techniques. In many cases, however, (for example, in chess or checkers), a straightforward use of the functional equation is impossible because of dimensionality difficulties. In circumventing these obstacles to effective computational solution, we employed a decomposition technique which we called “stratification” [1, 3]. In this paper, we present a different way of avoiding the dimensionality problem, based upon the concept of “extended state variable”. To indicate the utility of the concept, we shall apply it to the problem of finding a fault in a complex system.

Meshless Collocation Method for Inverse Source Identification Problems

2015 ◽  
Vol 7 (4) ◽  
pp. 496-509 ◽  
Author(s):  
Fuzhang Wang ◽  
Zhaoxing Ma
Keyword(s):  
Collocation Method ◽  
Radial Basis Functions ◽  
Source Identification ◽  
Basis Functions ◽  
Computationally Efficient ◽  
Identification Problems ◽  
Present Scheme ◽  
Numerical Examples ◽  
Meshless Collocation ◽  
Boundary Measurements

AbstractA novel meshless scheme is proposed for inverse source identification problems of Helmholtz-type equations. It is formulated by the non-singular general solutions of the Helmholtz-type equations augmented with radial basis functions. Under this meshless scheme, we can determine smooth source terms from partially accessible boundary measurements with accurate results. Numerical examples are presented to verify validity and accuracy of the present scheme. It is demonstrated that the present scheme is simple, accurate, stable and computationally efficient for inverse smooth source identification problems.

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