Add to ORKGIn previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the systemPeer Reviewe
A new class of neural fuzzy network based on a general neuron model is introduced in this paper. The...
This paper proposes a neural network for building and optimizing fuzzy models. The network can be re...
Abstract- Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerf...
In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe th...
Fuzzy neural networks are a connecting link between fuzzy logic and neurocomputing. The goal of this...
Researching artificial intelligence there are two areas... In this report it is assumed that the rea...
The incorporation of prior knowledge into neural networks can improve neural network learning in sev...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Artificial neural networks (ANN) have the ability to model input-output relationships from processin...
A new class of neural fuzzy network based on a general neuron model is introduced in this paper. The...
This paper proposes a neural network for building and optimizing fuzzy models. The network can be re...
Abstract- Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerf...
In previous works, we have presented two methodologies to obtain fuzzy rules in order to describe th...
Fuzzy neural networks are a connecting link between fuzzy logic and neurocomputing. The goal of this...
Researching artificial intelligence there are two areas... In this report it is assumed that the rea...
The incorporation of prior knowledge into neural networks can improve neural network learning in sev...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
Artificial neural networks (ANN) have the ability to model input-output relationships from processin...
A new class of neural fuzzy network based on a general neuron model is introduced in this paper. The...
This paper proposes a neural network for building and optimizing fuzzy models. The network can be re...
Abstract- Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerf...