Wavelets and Elman Neural Networks for monitoring environmental variables

Ciarlini, P.; Maniscalco, Umberto

doi:10.1016/j.cam.2007.10.040

Cited by 18 publications

(5 citation statements)

References 5 publications

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“…Different from RBF, BP and other feedforward neural networks, besides the input layer, output layer and hidden layer, Elman network still has a feedback layer of which the node number is equal to that of the hidden layer. The feedback layer acts as one-step delay operator, and is used to memorize the output value of hidden nodes [26][27] . Compared to the BP network, it involves faster training and has a simpler structure and higher accuracy [27] .…”

Section: Arithmetic Of Elman Neural Networkmentioning

confidence: 99%

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Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Wang

Zhang

Cui

et al. 2016

Chin. J. Mech. Eng.

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Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.0150.017 5 and sample error is controlled within 00.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis；the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

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Section: Arithmetic Of Elman Neural Networkmentioning

confidence: 99%

“…The feedback layer acts as one-step delay operator, and is used to memorize the output value of hidden nodes [26][27] . Compared to the BP network, it involves faster training and has a simpler structure and higher accuracy [27] . The structure of Elman neural network is shown in Fig.…”

Section: Arithmetic Of Elman Neural Networkmentioning

confidence: 99%

Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Wang

Zhang

Cui

et al. 2016

Chin. J. Mech. Eng.

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“…The activation function for hidden layer is one of the key factors which directly impact on network performance. The wavelet neural network [8][9] has the linear distribution of weights and convex of learning objective function, which can avoid local optimal nonlinear optimization problem. So the combination of wavelet and OHIF Elman can form a compact wavelet OHIF Elman NN shown in Fig.1.…”

Section: Wavelet Ohif Elman Nn Model Structurementioning

confidence: 99%

Wavelet OHIF Elman neural network model and its predictive control of processing quality

Liu

Yang

2010

SPIE Proceedings

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There are some difficulties in build the process quality prediction model based on Elman neural network. The traditional Sigmoid activation function often used in the hidden layer, while it is difficult to establish a quantitative relationship between the network size and resolution scale, therefore, a wavelet OHIF Elman neural network model is proposed in this paper, which full use of the neural network weights of the linear distribution and learning convex objective function, so it can avoid the local optimal nonlinear optimization problems. Simulation results show that the wavelet Elman OHIF Elman network decreased 12.9 percent compared with OHIF Elman network which used the sigmoid activation function in hidden layer.

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“…In a recent study of sensor selection for machine olfaction design [36], discrete wavelet transformation not only significantly reduced the number of sensor variables but also yielded an almost 100% accuracy for the classification of two types of odor (coffee and soda). Wavelet transformation coupled with artificial neural networks was also successfully used for electronic tongue design [37] and environmental variable monitoring [38]. To ensure the feature extraction effect of wavelet analysis, a couple of things should be taken into account including wavelet type and decomposition level.…”

Section: Feature Extraction and Dimension Reductionmentioning

confidence: 99%

Machine Learning: A Crucial Tool for Sensor Design

et al. 2008

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Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

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Wavelets and Elman Neural Networks for monitoring environmental variables

Cited by 18 publications

References 5 publications

Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Wavelet OHIF Elman neural network model and its predictive control of processing quality

Machine Learning: A Crucial Tool for Sensor Design

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