In this research, neural network (NN) and genetic algorithm (GA) are used together to design optimal NN structure. The proposed approach combines the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications in design and manufacturing. Genetic input selection approach is introduced to obtain optimal NN topology. Experimental results are given to evaluate the performance of the proposed system
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
. For many applications feedforward neural networks have proved to be a valuable tool. Although the ...
For many applications feedforward neural networks have proved to be a valuable tool. Although the ba...
The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating a...
In the paper we address the design and selection of products in the framework of genetic algorithms ...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
We present a general and systematic method for neural network design based on the genetic algorithm....
This paper describes the use of an evolutionary design system known as GANNET to synthesize the stru...
In the paper we address the design and selection of products in the framework of genetic algorithms ...
Abstract. This work deals with methods for finding optimal neural network architectures to learn par...
Abstract: The artificial neural networks (ANN) have proven their efficiency in several applications:...
This paper presents the tuning of the structure and parameters of a neural network using an improved...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
It has been demonstrated that genetic algorithms (GAs) can help search the global (or near global) o...
Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of ...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
. For many applications feedforward neural networks have proved to be a valuable tool. Although the ...
For many applications feedforward neural networks have proved to be a valuable tool. Although the ba...
The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating a...
In the paper we address the design and selection of products in the framework of genetic algorithms ...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
We present a general and systematic method for neural network design based on the genetic algorithm....
This paper describes the use of an evolutionary design system known as GANNET to synthesize the stru...
In the paper we address the design and selection of products in the framework of genetic algorithms ...
Abstract. This work deals with methods for finding optimal neural network architectures to learn par...
Abstract: The artificial neural networks (ANN) have proven their efficiency in several applications:...
This paper presents the tuning of the structure and parameters of a neural network using an improved...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
It has been demonstrated that genetic algorithms (GAs) can help search the global (or near global) o...
Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of ...
In this paper, the ability of genetic algorithms in designing artificial neural network (ANN) is inv...
. For many applications feedforward neural networks have proved to be a valuable tool. Although the ...
For many applications feedforward neural networks have proved to be a valuable tool. Although the ba...