scholarly journals Solving Multiple Objective Programming Problems Using Feed-Forward Artificial Neural Networks: The Interactive FFANN Procedure

1996 ◽  
Vol 42 (6) ◽  
pp. 835-849 ◽  
Author(s):  
Minghe Sun ◽  
Antonie Stam ◽  
Ralph E. Steuer
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Objective Programming ◽  
Multiple Objective ◽  
Feed Forward ◽  
Artificial Neural ◽  
Objective Programming

scholarly journals A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mehmet Hacibeyoglu ◽  
Mohammed H. Ibrahim
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Artificial Neural

Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feed-forward artificial neural networks training.

Contributions to Predict the Malfunction Probability of the Human-Machine-Environment System, Using Artificial Neural Networks

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Manufacturing Processes ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Linear Relationships ◽  
The Neural Network ◽  
Artificial Neural

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.

Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

2016 ◽  
Vol 231 (11) ◽  
pp. 2005-2016 ◽  
Author(s):  
Adil Baykasoğlu ◽  
Cengiz Baykasoğlu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Functionally Graded ◽  
Design Parameters ◽  
Multiple Objective ◽  
Circular Tubes ◽  
Crashworthiness Optimization ◽  
Artificial Neural ◽  
Graded Thickness

The objective of this paper is to develop a multiple objective optimization procedure for crashworthiness optimization of circular tubes having functionally graded thickness. The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; artificial neural networks for predicting objective functions values (peak crash force and specific energy absorption) for design parameters; and genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The proposed approach seaminglesly integrates artificial neural networks and genetic algorithms. Artificial neural network acts as an objective function evaluator within the multiple objective genetic algorithms. We have shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the finite element results.

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