Add to ORKGAbstract: The artificial neural networks (ANN) have proven their efficiency in several applications: pattern recognition, voice and classification problems. The training stage is very important in the ANN’s performance. The selection of the architecture of a neural network suitable to solve a given problem is one of the most important aspects of neural network research. The choice of the hidden layers number and the values of weights has a large impact on the convergence of the training algorithm. In this paper we propose a mathematical formulation in order to determine the optimal number of hidden layers and good values of weights. To solve this problem, we use genetic algorithms. The numerical results assess the effectiveness of the theor...
parameters design for full-automation ability is an extremely important task, therefore it is challe...
Feedforward neural networks are the most commonly used function approximation techniques in neural n...
The performance of an Artificial Neural Network (ANN) strongly depends on its hidden layer architect...
Abstract- Artificial Neural Networks have a number of properties which make them psuitable to solve ...
The multilayer perceptron has a large wide of classification and regression applications in many fie...
It has been demonstrated that genetic algorithms (GAs) can help search the global (or near global) o...
Abstract. This work deals with methods for finding optimal neural network architectures to learn par...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
The chapter presents a novel neural learning methodology by using different combination strategies f...
The chapter presents a novel neural learning methodology by using different combination strategies f...
The multilayer perceptron has a large wide of classification and regression applications in many fie...
Genetic algorithms are computational techniques for search, optimization and machine learning that a...
Feedforward neural networks are the most commonly used function approximation techniques in neural n...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
parameters design for full-automation ability is an extremely important task, therefore it is challe...
Feedforward neural networks are the most commonly used function approximation techniques in neural n...
The performance of an Artificial Neural Network (ANN) strongly depends on its hidden layer architect...
Abstract- Artificial Neural Networks have a number of properties which make them psuitable to solve ...
The multilayer perceptron has a large wide of classification and regression applications in many fie...
It has been demonstrated that genetic algorithms (GAs) can help search the global (or near global) o...
Abstract. This work deals with methods for finding optimal neural network architectures to learn par...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
The chapter presents a novel neural learning methodology by using different combination strategies f...
The chapter presents a novel neural learning methodology by using different combination strategies f...
The multilayer perceptron has a large wide of classification and regression applications in many fie...
Genetic algorithms are computational techniques for search, optimization and machine learning that a...
Feedforward neural networks are the most commonly used function approximation techniques in neural n...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
parameters design for full-automation ability is an extremely important task, therefore it is challe...
Feedforward neural networks are the most commonly used function approximation techniques in neural n...
The performance of an Artificial Neural Network (ANN) strongly depends on its hidden layer architect...