[[abstract]]In this study, we proposed an alternative operation of fuzzy arithmetic on L-R fuzzy numbers by three parameters of mode, left spread and right spread. Then, based on this approximation method, a new learning algorithm of a fully fuzzified neural network was developed in which the L-R fuzzy numbers were considered as the fuzzy signals. While the forward operations of fuzzy signals were based on the proposed three-parameter fuzzy arithmetic approximation method, the backward learning adopted a back-propagation learning procedure with a measurable error function. The learning algorithm was illustrated by an example of the recognition of fuzzy IF-THEN rules. The simulation result showed that the proposed approximation method used i...
A new class of neural fuzzy network based on a general neuron model is introduced in this paper. The...
This paper presents an improved fuzzy-neural network (FNN) model, which is simple but effective fuzz...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the...
Abstract–The general fuzzy numbers are approximately represented as polygonal fuzzy numbers, which c...
This paper presents a practical algorithm for training neural networks with fuzzy number weights, in...
training set, neural network, approximation re-sults Ordered fuzzy numbers as generalization of conv...
Researching artifical intelligence there are two areas especially up-to-date. On the one hand there ...
This dissertation proposes a fuzzy-arithmetic-based method for extracting fuzzy inference systems fr...
In our fuzzy neural networks fuzzy weights and fuzzy operations are used for training crisp and fuzz...
In this paper, a hybrid Fuzzy Neural Network (FNN) system for function approximation is presented. T...
Few training techniques are available for neural networks with fuzzy number weights, inputs, and out...
Fuzzy neural networks are a connecting link between fuzzy logic and neurocomputing. The goal of this...
In this paper, a new method which can alter the values of the parameters in neural networks is propo...
AbstractWe derive a general learning algorithm for training a fuzzified feedforward neural networks ...
A new class of neural fuzzy network based on a general neuron model is introduced in this paper. The...
This paper presents an improved fuzzy-neural network (FNN) model, which is simple but effective fuzz...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
A fuzzified neural network copes with fuzzy signals and/or weights so that the information about the...
Abstract–The general fuzzy numbers are approximately represented as polygonal fuzzy numbers, which c...
This paper presents a practical algorithm for training neural networks with fuzzy number weights, in...
training set, neural network, approximation re-sults Ordered fuzzy numbers as generalization of conv...
Researching artifical intelligence there are two areas especially up-to-date. On the one hand there ...
This dissertation proposes a fuzzy-arithmetic-based method for extracting fuzzy inference systems fr...
In our fuzzy neural networks fuzzy weights and fuzzy operations are used for training crisp and fuzz...
In this paper, a hybrid Fuzzy Neural Network (FNN) system for function approximation is presented. T...
Few training techniques are available for neural networks with fuzzy number weights, inputs, and out...
Fuzzy neural networks are a connecting link between fuzzy logic and neurocomputing. The goal of this...
In this paper, a new method which can alter the values of the parameters in neural networks is propo...
AbstractWe derive a general learning algorithm for training a fuzzified feedforward neural networks ...
A new class of neural fuzzy network based on a general neuron model is introduced in this paper. The...
This paper presents an improved fuzzy-neural network (FNN) model, which is simple but effective fuzz...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...