A diffusion-neural-network for learning from small samples
Neural information processing models largely assume that the patterns for training a neural
network are sufficient. Otherwise, there must exist a non-negligible error between the real …
network are sufficient. Otherwise, there must exist a non-negligible error between the real …
Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms
KP Ferentinos - Neural networks, 2005 - Elsevier
Two neural network (NN) applications in the field of biological engineering are developed,
designed and parameterized by an evolutionary method based on the evolutionary process …
designed and parameterized by an evolutionary method based on the evolutionary process …
A novel type of activation function in artificial neural networks: Trained activation function
ÖF Ertuğrul - Neural Networks, 2018 - Elsevier
Determining optimal activation function in artificial neural networks is an important issue
because it is directly linked with obtained success rates. But, unfortunately, there is not any …
because it is directly linked with obtained success rates. But, unfortunately, there is not any …
A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm
In this research, a new metaheuristic optimization algorithm, inspired by biological nervous
systems and artificial neural networks (ANNs) is proposed for solving complex optimization …
systems and artificial neural networks (ANNs) is proposed for solving complex optimization …
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
JT Tsai, JH Chou, TK Liu - IEEE Transactions on Neural …, 2006 - ieeexplore.ieee.org
In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the problem of
tuning both network structure and parameters of a feedforward neural network. The HTGA …
tuning both network structure and parameters of a feedforward neural network. The HTGA …
The Chebyshev-polynomials-based unified model neural networks for function approximation
TT Lee, JT Jeng - IEEE Transactions on Systems, Man, and …, 1998 - ieeexplore.ieee.org
In this paper, we propose the approximate transformable technique, which includes the
direct transformation and indirect transformation, to obtain a Chebyshev-Polynomials-Based …
direct transformation and indirect transformation, to obtain a Chebyshev-Polynomials-Based …
An improved particle swarm optimization for evolving feedforward artificial neural networks
This paper presents a new evolutionary artificial neural network (ANN) algorithm named
IPSONet that is based on an improved particle swarm optimization (PSO). The improved …
IPSONet that is based on an improved particle swarm optimization (PSO). The improved …
A linear model based on Kalman filter for improving neural network classification performance
Neural network has been applied in several classification problems such as in medical
diagnosis, handwriting recognition, and product inspection, with a good classification …
diagnosis, handwriting recognition, and product inspection, with a good classification …
[引用][C] The 2012 International Joint Conference on Neural Networks (IJCNN)
[Title page] Page 1 The 2012 International Joint Conference on Neural Networks (IJCNN)
Brisbane, Australia (June 10-‐15, 2012) IEEE Catalogue Number: CFP12IJS-ART ISBN: 978-1-4673-1490-9 …
Brisbane, Australia (June 10-‐15, 2012) IEEE Catalogue Number: CFP12IJS-ART ISBN: 978-1-4673-1490-9 …