Prior knowledge for fuzzy knowledge-based artificial neural networks from fuzzy set covering

2005 ◽  
Author(s):  
J. van Zyl ◽  
I. Cloete
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Fuzzy Set ◽  
Set Covering ◽  
Knowledge Based ◽  
Artificial Neural ◽  
Fuzzy Knowledge

scholarly journals Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Dual Frequency ◽  
Knowledge Based ◽  
Slot Size ◽  
Good Agreement

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Incorporating prior knowledge into artificial neural networks—an industrial case study

2004 ◽  
Vol 62 ◽  
pp. 131-151 ◽  
Author(s):  
Srečko Milanič ◽  
Stanko Strmčnik ◽  
Davorka Šel ◽  
Nadja Hvala ◽  
Rihard Karba
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Industrial Case Study ◽  
Artificial Neural

Integrating knowledge-based systems and artificial neural networks for engineering

1995 ◽  
Vol 9 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Nabil Kartam ◽  
Ian Flood ◽  
Tanit Tongthong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Generalized Solution ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Engineering Problems ◽  
Scope Of Application ◽  
Artificial Neural ◽  
The Individual

AbstractThe feasibility and relative merits of integrating knowledge-based systems (KBSs) and artificial neural networks (ANNs) for application to engineering problems are presented and evaluated. The strength of KBSs lies in their ability to represent human judgment and solve problems by providing explanations from and reasoning with heuristic knowledge. ANNs demonstrate problem solving characteristics not inherent in KBSs, including an ability to learn from example, develop a generalized solution applicable to a range of examples of the problem, and process information extremely rapidly. In this respect, KBSs and ANNs are complementary, rather than alternatives, and may be integrated into a system that exploits the advantages of both technologies. The scope of application and quality of solutions produced by such a hybrid extend beyond the boundaries of the individual technologies. This paper identifies and describes how KBSs and ANNs can be integrated, and provides an evaluation of the advantages that will accrue in engineering applications.

The Modeling of Interval-Valued Time Series: A Method Based on Fuzzy Set Theory and Artificial Neural Networks

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002
Author(s):  
Wei Zhou ◽  
Dan Shan ◽  
Jianhua Yang ◽  
Wei Lu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Experimental Studies ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Interval Valued

Interval-valued time series (ITS) are interval-valued data that are collected in chronological order. The modeling of ITS is an ongoing issue in domain of time series analysis. This paper presents a new modeling method of ITS based on the synergy of fuzzy set theory and artificial neural networks. The proposed method involves the construction of collection of fuzzy sets describing characteristics of amplitude of ITS, the expression and reconstruction mechanism of ITS and the emergence of model of ITS based on artificial neural network (ANN). The resulting model of ITS not only supports the linguistic output but also the numeric output in interval format. A series of experimental studies is reported for two publicly available financial datasets showing different dynamic characteristics. Experimental results clearly show that the constructed ITS model has the better performance on the linguistic and numeric level.

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