Face recognition using a fuzzy-Gaussian neural network

Neagoe, Victor-Emil; Iatan, Iuliana F.

doi:10.1109/coginf.2002.1039318

Cited by 9 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…are some overlapping factors (see [6]),  R 1 is the recognition rate (%) for the training lot (obtained at the end of the training),  R 2 is the recognition rate (%) for the test lot (corresponding to the last epoch of training). )-CFGNN.…”

Section: Resultsmentioning

confidence: 99%

“…The four-layer structure of the Fuzzy-Gaussian Neural Network (FGNN) is shown in [6]. It is a special type of neural network, by special type of FGNN understanding that it has so special the connections (between the second and third layers) and the operations with the nodes, too.…”

Section: Fuzzy Gaussian Neural Network (Fgnn)mentioning

confidence: 99%

“…The connections between the membership neurons of the second layer and the rule neurons that characterize the third layer of the FGNN indicate the premise of the fuzzy rules. This layer computes the antecedent matching by the product operation [6].…”

Section: Fuzzy Gaussian Neural Network (Fgnn)mentioning

confidence: 99%

“…These parameters of the FGNN one initialize according to an on-line initialization algorithm (see [6]) and they will be refined during the training algorithm.…”

Section: Fuzzy Gaussian Neural Network (Fgnn)mentioning

confidence: 99%

See 3 more Smart Citations

A Concurrent Fuzzy Neural Network Approach for a Fuzzy Gaussian Neural Network

Iatan¹

2014

Proceedings of 10th World Congress on Computational Mechanics

View full text Add to dashboard Cite

Abstract. The aim of the paper is to introduce a concurrent fuzzy neural network approach, representing a winner-takes-all collection of fuzzy FUZZY GAUSSIAN NEURAL NETWORK (FGNN)The four-layer structure of the Fuzzy-Gaussian Neural Network (FGNN) is shown in [6]. It is a special type of neural network, by special type of FGNN understanding that it has so special the connections (between the second and third layers) and the operations with the nodes, too.It represents a modified version of Chen and Teng fuzzy neural network, by transforming the function of approximation into a function of classification. The change affects only the equations of the fourth layer, but the structure diagram is similar.The FGNN keeps the advantages of the original fuzzy net described by Chen and Teng [3] for identification in control systems:(a) its structure allows us to construct the fuzzy system rule by rule; (b) if the prior knowledge of an expert is available, then we can directly add some rule nodes and term nodes; (c) the number of rules do not increase exponentially with the number of inputs; (d) elimination of redundant nodes rule by rule. Its construction is based on fuzzy rules of the form:

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Fuzzy Gaussian Neural Network (Fgnn)mentioning

confidence: 99%

Section: Fuzzy Gaussian Neural Network (Fgnn)mentioning

confidence: 99%

“…These parameters of the FGNN one initialize according to an on-line initialization algorithm (see [6]) and they will be refined during the training algorithm.…”

Section: Fuzzy Gaussian Neural Network (Fgnn)mentioning

confidence: 99%

See 2 more Smart Citations

A Concurrent Fuzzy Neural Network Approach for a Fuzzy Gaussian Neural Network

Iatan¹

2014

Proceedings of 10th World Congress on Computational Mechanics

View full text Add to dashboard Cite

show abstract

“…The FGNN parameters one initialize according to the on-line initialization algorithm [2], [6], [9] and they will be refined during the training algorithm [2], [6][7][8][9].…”

mentioning

confidence: 99%