This paper presents a practical algorithm for training neural networks with fuzzy number weights, inputs, and outputs. Typically, fuzzy number neural networks are difficult to train because of the many α-cut constraints implied by the fuzzy weights. A transformation is used to eliminate these constraints and allow use of standard unconstrained optimization methods. The algorithm is demonstrated on a three-layer network
We present a novel training algorithm for a feed forward neural network with a single hidden layer o...
[[abstract]]In this study, we proposed an alternative operation of fuzzy arithmetic on L-R fuzzy num...
The purpose of this article is to present general training strategies for training fuzzy number neur...
Few training techniques are available for neural networks with fuzzy number weights, inputs, and out...
In a fuzzy number neural network, the inputs, weights, and outputs are general fuzzy numbers. The re...
In our fuzzy neural networks fuzzy weights and fuzzy operations are used for training crisp and fuzz...
Fuzzy neural networks are a connecting link between fuzzy logic and neurocomputing. The goal of this...
In this paper, we describe a method for nonlinear fuzzy regression using neural network models. In e...
This paper deals with the possibility of learning the neural networks by the use of training pattern...
A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the...
AbstractWe derive a general learning algorithm for training a fuzzified feedforward neural networks ...
Abstract–The general fuzzy numbers are approximately represented as polygonal fuzzy numbers, which c...
We propose a constructive method, inspired by Simpson's min-max technique (1992), for obtaining fuzz...
Abstract: A learning algorithm based on a gradient technique is introduced for the algebraic fuzzy ...
training set, neural network, approximation re-sults Ordered fuzzy numbers as generalization of conv...
We present a novel training algorithm for a feed forward neural network with a single hidden layer o...
[[abstract]]In this study, we proposed an alternative operation of fuzzy arithmetic on L-R fuzzy num...
The purpose of this article is to present general training strategies for training fuzzy number neur...
Few training techniques are available for neural networks with fuzzy number weights, inputs, and out...
In a fuzzy number neural network, the inputs, weights, and outputs are general fuzzy numbers. The re...
In our fuzzy neural networks fuzzy weights and fuzzy operations are used for training crisp and fuzz...
Fuzzy neural networks are a connecting link between fuzzy logic and neurocomputing. The goal of this...
In this paper, we describe a method for nonlinear fuzzy regression using neural network models. In e...
This paper deals with the possibility of learning the neural networks by the use of training pattern...
A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the...
AbstractWe derive a general learning algorithm for training a fuzzified feedforward neural networks ...
Abstract–The general fuzzy numbers are approximately represented as polygonal fuzzy numbers, which c...
We propose a constructive method, inspired by Simpson's min-max technique (1992), for obtaining fuzz...
Abstract: A learning algorithm based on a gradient technique is introduced for the algebraic fuzzy ...
training set, neural network, approximation re-sults Ordered fuzzy numbers as generalization of conv...
We present a novel training algorithm for a feed forward neural network with a single hidden layer o...
[[abstract]]In this study, we proposed an alternative operation of fuzzy arithmetic on L-R fuzzy num...
The purpose of this article is to present general training strategies for training fuzzy number neur...
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