Add to ORKGMany works in the area of hybrid neural learning algorithms combine global and local based method for artificial neural network. In this paper, we discuss some special properties of a hybrid neural learning algorithm that combines the GA based method with least square based methods such as QR factorization. We look at different types of learning properties of this new hybrid algorithm, such as time complexity, convergence property, and the stability of the algorithm.C
Since the introduction of the backpropagation algorithm as a learning rule for neural networks much ...
This paper describes a method for searching near-optimal neural networks using Genetic Algorithms. T...
Considering computational algorithms available in the literature, associated with supervised learnin...
In the last few years, there have been many works in the area of hybrid neural learning algorithms c...
A hybrid algorithm combining Artificial Bee Colony (ABC) algorithm with Levenberq-Marquardt (LM) alg...
In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionar...
: This paper describes two algorithms based on cooperative evolution of internal hidden network repr...
Considering computational algorithms available in the literature, associated with supervised learnin...
Evolutionary learning of neural architectures has been extensively studied with mixed results. Here ...
mended hybrid learning algorithm for radial basis function neural networks to improve generalization...
Backpropagation algorithm is a classical technique used in the training of the artificial neural net...
In this study we investigated a hybrid model based on the Discrete Gradient method and an evolutiona...
The chapter presents a novel neural learning methodology by using different combination strategies f...
In this paper, a new weight-setting method is proposed to improve the training time and generalizati...
In this paper, we propose a hybrid learning algorithm for the single hidden layer feedforward neural...
Since the introduction of the backpropagation algorithm as a learning rule for neural networks much ...
This paper describes a method for searching near-optimal neural networks using Genetic Algorithms. T...
Considering computational algorithms available in the literature, associated with supervised learnin...
In the last few years, there have been many works in the area of hybrid neural learning algorithms c...
A hybrid algorithm combining Artificial Bee Colony (ABC) algorithm with Levenberq-Marquardt (LM) alg...
In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionar...
: This paper describes two algorithms based on cooperative evolution of internal hidden network repr...
Considering computational algorithms available in the literature, associated with supervised learnin...
Evolutionary learning of neural architectures has been extensively studied with mixed results. Here ...
mended hybrid learning algorithm for radial basis function neural networks to improve generalization...
Backpropagation algorithm is a classical technique used in the training of the artificial neural net...
In this study we investigated a hybrid model based on the Discrete Gradient method and an evolutiona...
The chapter presents a novel neural learning methodology by using different combination strategies f...
In this paper, a new weight-setting method is proposed to improve the training time and generalizati...
In this paper, we propose a hybrid learning algorithm for the single hidden layer feedforward neural...
Since the introduction of the backpropagation algorithm as a learning rule for neural networks much ...
This paper describes a method for searching near-optimal neural networks using Genetic Algorithms. T...
Considering computational algorithms available in the literature, associated with supervised learnin...