Add to ORKGIn this paper, we present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on our previous work with discrete-valued data [3]. The rules learned can then be used in a neural network to predict the function value based upon its dependent variables. An example is shown of learning a control system function
This paper examines the underlying relationship between radial basis function artificial neural netw...
Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then &qu...
This paper briefly reviews techniques for learning fuzzy rules. In many applications fuzzy if-then r...
In this paper, we present a method for the induction of fuzzy logic rules to predict a numerical fun...
We present a method for learning fuzzy logic membership functions and rules to approximate a numeric...
A three-step method for function approximation with a fuzzy system is proposed. First, the membershi...
A three-step method for function approximation with a fuzzy system is proposed. First, the membershi...
This thesis describes the architecture of learning systems which can explain their decisions through...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
AbstractWhereas conventional fuzzy reasoning lacks determining membership functions, a neural networ...
Fuzzy systems can be used for function approximation based on a set of linguistic rules. We present ...
[[abstract]]Fuzzy modeling is the task of identifying the structure and parameters of a fuzzy if-the...
[[abstract]]Presents an innovative approach to rule extraction directly from experimental numerical ...
Artificial neural networks have been successfully applied to solve a variety of business application...
The fuzzy controller (FC) consists of two parts. First one is the control rule part which is referre...
This paper examines the underlying relationship between radial basis function artificial neural netw...
Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then &qu...
This paper briefly reviews techniques for learning fuzzy rules. In many applications fuzzy if-then r...
In this paper, we present a method for the induction of fuzzy logic rules to predict a numerical fun...
We present a method for learning fuzzy logic membership functions and rules to approximate a numeric...
A three-step method for function approximation with a fuzzy system is proposed. First, the membershi...
A three-step method for function approximation with a fuzzy system is proposed. First, the membershi...
This thesis describes the architecture of learning systems which can explain their decisions through...
Fuzzy inference systems and neural networks both provide mathematical systems for approximating cont...
AbstractWhereas conventional fuzzy reasoning lacks determining membership functions, a neural networ...
Fuzzy systems can be used for function approximation based on a set of linguistic rules. We present ...
[[abstract]]Fuzzy modeling is the task of identifying the structure and parameters of a fuzzy if-the...
[[abstract]]Presents an innovative approach to rule extraction directly from experimental numerical ...
Artificial neural networks have been successfully applied to solve a variety of business application...
The fuzzy controller (FC) consists of two parts. First one is the control rule part which is referre...
This paper examines the underlying relationship between radial basis function artificial neural netw...
Extracting fuzzy rules from data allows relationships in the data to be modeled by "if-then &qu...
This paper briefly reviews techniques for learning fuzzy rules. In many applications fuzzy if-then r...