Nature-Inspired Optimization Algorithm-Tuned Feed-Forward and Recurrent Neural Networks Using CFD-Based Phenomenological Model-Generated Data to Model the EBW Process

2019 ◽  
Vol 45 (4) ◽  
pp. 2779-2797 ◽  
Author(s):  
Debasish Das ◽  
Abhishek Rudra Pal ◽  
Amit Kumar Das ◽  
Dilip Kumar Pratihar ◽  
Gour Gopal Roy
Keyword(s):  
Neural Networks ◽  
Optimization Algorithm ◽  
Phenomenological Model ◽  
Recurrent Neural Networks ◽  
Feed Forward

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.

TRAINING RECURRENT NEURAL NETWORKS — THE MINIMAL TRAJECTORY ALGORITHM

1992 ◽  
Vol 03 (01) ◽  
pp. 83-101 ◽  
Author(s):  
D. Saad
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Learning Rule ◽  
Weight Matrix ◽  
Training Methods ◽  
Input Output ◽  
Feed Forward ◽  
End Point ◽  
Common Weight ◽  
Perceptron Learning

The Minimal Trajectory (MINT) algorithm for training recurrent neural networks with a stable end point is based on an algorithmic search for the systems’ representations in the neighbourhood of the minimal trajectory connecting the input-output representations. The said representations appear to be the most probable set for solving the global perceptron problem related to the common weight matrix, connecting all representations of successive time steps in a recurrent discrete neural networks. The search for a proper set of system representations is aided by representation modification rules similar to those presented in our former paper,1 aimed to support contributing hidden and non-end-point representations while supressing non-contributing ones. Similar representation modification rules were used in other training methods for feed-forward networks,2–4 based on modification of the internal representations. A feed-forward version of the MINT algorithm will be presented in another paper.5 Once a proper set of system representations is chosen, the weight matrix is then modified accordingly, via the Perceptron Learning Rule (PLR) to obtain the proper input-output relation. Computer simulations carried out for the restricted cases of parity and teacher-net problems show rapid convergence of the algorithm in comparison with other existing algorithms, together with modest memory requirements.

scholarly journals Approximation of phenol concentration using novel hybrid computational intelligence methods

2014 ◽  
Vol 24 (1) ◽  
pp. 165-181 ◽  
Author(s):  
Pawel Plawiak ◽  
Ryszard Tadeusiewicz
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Computational Intelligence ◽  
Recurrent Neural Networks ◽  
Phenol Concentration ◽  
Neural Systems ◽  
Feed Forward ◽  
Computational Intelligence Methods ◽  
Lm Algorithm ◽  
Selection Of

Abstract This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

Sign in / Sign up

Export Citation Format

Share Document