Optimization of Wavelet Neural Network Model for Tide Prediction Based on Genetic Algorithm

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

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Genetic algorithm based artificial neural network model for voltage stability monitoring

2014 21st International Conference on Telecommunications (ICT) ◽

10.1109/npsc.2014.7103798 ◽

2014 ◽

Cited By ~ 4

Author(s):

K S Sajan ◽

Barjeev Tyagi ◽

Vishal Kumar

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Voltage Stability ◽

Stability Monitoring ◽

Artificial Neural

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An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060420000219 ◽

2020 ◽

Vol 34 (3) ◽

pp. 430-443

Author(s):

A. Saravanan ◽

J. Jerald ◽

A. Delphin Carolina Rani

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Network Model ◽

Neural Network Model ◽

Network Models ◽

Manufacturing Cost ◽

Genetic Operators ◽

Neural Network Models ◽

Data Set ◽

The Neural Network

AbstractThe objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

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