Generating optimal topologies in structural design using a homogenization method
Computer Methods in Applied Mechanics and Engineering
Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks
Advances in neural information processing systems 2
Approximation capabilities of multilayer feedforward networks
Neural Networks
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
IEEE Transactions on Computers
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Shape Optimization Under Uncertainty—A Stochastic Programming Perspective
SIAM Journal on Optimization
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Image Processing
Structural dynamic topology optimization based on dynamic reliability using equivalent static loads
Structural and Multidisciplinary Optimization
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This research explores the usage of classification approaches in order to facilitate the accurate estimation of probabilistic constraints in optimization problems under uncertainty. The efficiency of the proposed framework is achieved with the combination of a conventional topology optimization method and a classification approach- namely, probabilistic neural networks (PNN). Specifically, the implemented framework using PNN is useful in the case of highly nonlinear or disjoint failure domain problems. The effectiveness of the proposed framework is demonstrated with three examples. The first example deals with the estimation of the limit state function in the case of disjoint failure domains. The second example shows the efficacy of the proposed method in the design of stiffest structure through the topology optimization process with the consideration of random field inputs and disjoint failure phenomenon, such as buckling. The third example demonstrates the applicability of the proposed method in a practical engineering problem.