We show how to train neural nets, with certain sign constraints on their weights, using genetic algorithms, to approximate solutions to systems of fuzzy linear ...
We will show how to train neural nets, with certain sign constraints on their weights, using evolutionary algorithms, to approximate solutions to systems of ...
Missing: approximations | Show results with:approximations
This paper continues our research ([1]-[4],[6],[11]) into using neural nets to solve fuzzy problems. We will show how to train neural nets, with certain ...
It is shown how to train neural nets, with certain sign constraints on their weights, using evolutionary algorithms, to approximate solutions to systems of ...
Missing: approximations | Show results with:approximations
We show how a neural net, with sign restrictions on its weights, can be trained to produce approximate solutions to fuzzy linear programming problems.
Missing: approximations | Show results with:approximations
In this paper, we first propose an architecture of fuzzy neural network. (FNN) with fuzzy weights for fuzzy input vectors and fuzzy targets to find approximate ...
We show how a neural net, with sign restrictions on its weights, can be trained to produce approximate solutions to fuzzy linear programming problems.
In this paper we show how a neural net can be used to solve AX = C , for X , even though for some values of A and C there is no fuzzy arithmetic solution ...
Missing: approximations | Show results with:approximations
We will show how to train neural nets, with certain sign constraints on their weights, us- ing evolutionary algorithms, to approximate solutions to systems oj ...
Missing: approximations | Show results with:approximations
In this paper, a new hybrid method based on fuzzy neural network (FNN) for approximate solution of fuzzy linear systems of the form $$Ax=d,$$ where $$A$$ is ...
Missing: approximations | Show results with:approximations