An inverse vibration problem solved by a Multilayer Perceptron NeuralNetwork∗
نویسندگان
چکیده
The damage identification problem in mechanical structures is a process of determining parameters based on numerical analysis from a comparison on measurement data and the output data from a mathematical model. It is an important research topic. The main idea behind the damage identification problem, using displacement data, is that the structural damage will manifest a changing in the displacement time response of the system. Structural damage detection is displayed as an inverse vibration problem, since the damage evaluation is obtained through the determination of the stiffness coefficient variation. These problems are usually unstable – small variations in the input data, such as random errors inherent to the measurements used in the analysis, can cause large oscillations on the solution. The inverse problem, in general an illposed problem, is presented as a well-posed functional form, Eq. (1), whose solution is obtained through an optimization procedure
منابع مشابه
Model-basedtestcase Prioritizationusing Neuralnetwork Classification
Model-based testing for real-life software systems often require a large number of tests, all of which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance the tester perceives. In this paper, this problem is solved by improving our given previous study, namely, applying classification approach to the...
متن کاملInverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation
Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-...
متن کاملArtificial Neural Networks for the Solution of Inverse Problems
In this work we present a variational formulation for neural networks. Within this formulation, the learning problem for the multilayer perceptron lies in terms of finding a function which is an extremal for some functional. As we will see, a variational formulation for neural networks provides a direct method for the solution of general variational problems, in any dimension and up to any degr...
متن کاملAre Rosenblatt multilayer perceptrons more powerfull than sigmoidal multilayer perceptrons? From a counter example to a general result
In the eighties the problem of the lack of an efficient algorithm to train multilayer Rosenblatt perceptrons was solved by sigmoidal neural networks and backpropagation. But should we still try to find an efficient algorithm to train multilayer hardlimit neuronal networks, a task known as a NP-Complete problem? In this work we show that this would not be a waste of time by means of a counter ex...
متن کاملInverting feedforward neural networks using linear and nonlinear programming
The problem of inverting trained feedforward neural networks is to find the inputs which yield a given output. In general, this problem is an ill-posed problem because the mapping from the output space to the input space is a one-to-many mapping. In this paper, we present a method for dealing with the inverse problem by using mathematical programming techniques. The principal idea behind the me...
متن کامل