Artificial neural network weights optimization using ICA, GA, ICA-GA and R-ICA-GA: Comparing performances

Khorani, Vahid; Forouzideh, Nafiseh; Nasrabadi, Ali Motie

doi:10.1109/hima.2011.5953956

Cited by 12 publications

(1 citation statement)

References 7 publications

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“…But these methods also have some shortcomings. The artificial neural network method calculates the feature weights according to the training model and its connection weight value, which is poor to explain, and hard to be transplanted [35,36]. The principle of the water injection method is based on data correlation, which is strict to the types of data.…”

Section: Module Of Weight Optimizationmentioning

confidence: 99%

Development of Ground Special Vehicle PHM with Case-Based Reason Model

Qicai

Yuan

2021

Applied Sciences

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The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

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Section: Module Of Weight Optimizationmentioning

confidence: 99%