Fuzzy adaptive learning control network with on-line neural learning

Lin, Chin-Teng; Lin, Cheng‐Jian; Lee, C. S. George

doi:10.1016/0165-0114(94)00195-d

Cited by 70 publications

(34 citation statements)

References 18 publications

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“…In general, more an ANN has inputs and more the solving of the problem is complex, more it will be necessary to add neurons in the hidden layer [14].…”

Section: Choice Of the Ann Architecturementioning

confidence: 99%

Discharge Currents Discrimination Technique Based on Multi-Linear Regression Line and Artificial Neural Networks for Power Transformers Diagnosis

et al. 2013

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The proposed work will be consecrated to the study of positive pre-breakdown currents triggered in mineral transformer oil under 50 Hz alternating overvoltage. Since negative currents are recorded in low rates and for higher voltage levels than positive ones, only the latter will be prior taken into consideration. Both streamer propagation and arc discharge current types are identied and are used in the training process of an articial neural network and the multi-linear regression line of these currents in order to develop a complementary diagnosis tool that can serve as an on-line transformer protection. More successful results than those obtained by other developed techniques are expected.

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“…In general, more an ANN has inputs and more the solving of the problem is complex, more it will be necessary to add neurons in the hidden layer [14].…”

Section: Choice Of the Ann Architecturementioning

confidence: 99%

Discharge Currents Discrimination Technique Based on Multi-Linear Regression Line and Artificial Neural Networks for Power Transformers Diagnosis

et al. 2013

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“…The parameters set are updated through training data by gradient descent method ( see [3,6] ). We can see that most of the existing neural-network-based fuzzy systems are trained by the BP algorithm.…”

Section: Back Propagation Learning Algorithmmentioning

confidence: 99%

“…function approximation, system identification and control, image processing, time series prediction and so on [1][2][3]6].…”

Section: Introductionmentioning

confidence: 99%

“…Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. Well established time series models include : (1) linear models, e.g., moving average, exponential smoothing and the autoregressive intergrated moving average (ARIMA); (2) nonlinear models, e.g., neural network models and fuzzy system models [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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An experiment result based on adaptive neuro-fuzzy inference system for stock price

Cường¹,

Chien²

2012

JCC

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Tóm tȃt. Trong nhũ . ng nȃm cuối thi . tru .ò. ng tài chính thế gió . i dã thay dô ' i nhò . su .. phát triê ' n nhiề u hê . thống tiên tiến. Nhȃm khai thác dũ . liê . u thò . i gian thu .. c dã phát triê ' n nhũ . ng lĩnh vu . . c mó . i nhu . các hê . mò . no . ron dành cho bài toán du .. báo và nhu . vâ . y làm sống la . i quan tâm tó . i du .. báo các chı ' số tài chính và chú . ng khoán. Bài báo này gió . i thiê . u mô . t thu .' nghiê . m dùng hê . suy diễn mò . -no . ron vó . i mô . t quy trình tính toán mó . i dê ' du .. báo giá chú . ng khoán.Abstract. In the last years, the financial markets around the world have been modified by the rapid development of advance systems. The acquisition of high-frequency data in real times has developed new fields like neuro-fuzzy systems for forcasting problems, renewing also the interest in the forcasting of financial and stock market indexes. In this paper, we present an experiment result based on Adaptive Neuro-Fuzzy Inference System with a new computing procedure for stock price prediction.

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“…The FFD architecture is a five-layered feedforward networkbased fuzzy inference system. The connectionist structure is isomorphic to an NN, with layers corresponding to input states, decision states, and hidden layers that substitute the fuzzy inference engine in terms of aggregation, defuzzification, and decision making [43]. Fuzzy rules of the motor fault detection/diagnosis problem and membership functions used in these rules are implemented in the hidden-layer nodes.…”

Section: A Ffdmentioning

confidence: 99%

Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis

Altug

Chen²,

Trussell

1999

IEEE Trans. Ind. Electron.

156

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Fuzzy adaptive learning control network with on-line neural learning

Cited by 70 publications

References 18 publications

Discharge Currents Discrimination Technique Based on Multi-Linear Regression Line and Artificial Neural Networks for Power Transformers Diagnosis

Discharge Currents Discrimination Technique Based on Multi-Linear Regression Line and Artificial Neural Networks for Power Transformers Diagnosis

An experiment result based on adaptive neuro-fuzzy inference system for stock price

Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis

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