Scaled Conjugate Gradient Based Adaptive ANN Control for SVM-DTC Induction Motor Drive

Babani, Lochan; Jadhav, Sadhana V.; Chaudhari, B. N.

doi:10.1007/978-3-319-44944-9_33

Cited by 35 publications

(13 citation statements)

References 20 publications

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“…In contrast, both the LM and SCG algorithms were not significantly affected by an increase or decrease in the number of neurons and maintained a higher rapidity in generating solutions. The SCG uses second-order approximation, resulting in fewer iterations and faster learning 42 . This may be due to the algorithm using a step-size scaling mechanism that avoids a timewasting line search per learning iteration 43 , 44 .…”

Section: Resultsmentioning

confidence: 99%

A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

Hlangwani

Doorsamy

Adebiyi

et al. 2021

Sci Rep

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Bioprocess development for umqombothi (a South African traditional beer) as with other traditional beer products can be complex. As a result, beverage bioprocess development is shifting towards new systematic protocols of experimentation. Traditional optimization methods such as response surface methodology (RSM) require further comparison with a relevant machine learning system. Artificial neural network (ANN) is an effective non-linear multivariate tool in bioprocessing, with enormous generalization, prediction, and validation capabilities. ANN bioprocess development and optimization of umqombothi were done using RSM and ANN. The optimum condition values were 1.1 h, 29.3 °C, and 25.9 h for cooking time, fermentation temperature, and fermentation time, respectively. RSM was an effective tool for the optimization of umqombothi’s bioprocessing parameters shown by the coefficient of determination (R2) closer to 1. RSM significant parameters: alcohol content, total soluble solids (TSS), and pH had R2 values of 0.94, 0.93, and 0.99 respectively while the constructed ANN significant parameters: alcohol content, TSS, and viscosity had R2 values of 0.96, 0.96, and 0.92 respectively. The correlation between experimental and predicted values suggested that both RSM and ANN were suitable bioprocess development and optimization tools.

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Section: Resultsmentioning

confidence: 99%

A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

Hlangwani

Doorsamy

Adebiyi

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…It calculates the second-order information from the two first-order gradients of the parameters using all the training data [ 32 ]. Unlike other optimizers, this algorithm does not perform a line search in each iteration to make it more computationally efficient [ 33 ]. More detailed information about this algorithm can be found in [ 34 ].…”

Section: Damage Detection Methodologymentioning

confidence: 99%

Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Feijóo

Zambrano

Vidal

et al. 2021

Sensors

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Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

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“…After this, a line search mechanism is utilized to reduce the error function. In MATLAB, 'trainscg' is a network training function that uses the Scaled Conjugate Gradient (SGG) methodology to adjust weights and biases [50]. This could train a certain network that has derivative functions for the weight, net-input and transfer functions.…”

Section: Scaled Conjugate Gradient Training Algorithmmentioning

confidence: 99%

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

2021

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In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results.

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Scaled Conjugate Gradient Based Adaptive ANN Control for SVM-DTC Induction Motor Drive

Cited by 35 publications

References 20 publications

A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

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