Journal ArticleDOI
Damage Identification of Multimember Structure using Improved Neural Networks
M. Rajendra,K. Shankar +1 more
- Vol. 3, Iss: 3, pp 57-75
TLDR
The performance of the novel IRBF method is compared with the conventional RBF and Genetic algorithm methods and it is found to be a good multiple member damage identification strategy in terms of accuracy and precision with less computational effort.Abstract:
A novel two stage Improved Radial Basis Function (IRBF) neural network for the damage identification of a multimember structure in the frequency domain is presented. The improvement of the proposed IRBF network is carried out in two stages. Conventional RBF network is used in the first stage for preliminary damage prediction and in the second stage reduced search space moving technique is used to minimize the prediction error. The network is trained with fractional frequency change ratios (FFCs) and damage signature indices (DSIs) as effective input patterns and the corresponding damage severity values as output patterns. The patterns are searched at different damage levels by Latin hypercube sampling (LHS) technique. The performance of the novel IRBF method is compared with the conventional RBF and Genetic algorithm (GA) methods and it is found to be a good multiple member damage identification strategy in terms of accuracy and precision with less computational effort.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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Material Selection Using a Novel Multiple Attribute Decision Making Method
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TL;DR: A novel multiple attribute decision making (MADM) method for solving the material selection problem that considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of Importance.
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Pattern Recognition and Machine Learning
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Fast learning in networks of locally-tuned processing units
John Moody,Christian J. Darken +1 more
TL;DR: This work proposes a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988).
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