Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique

Ghritlahre, Harish Kumar; Prasad, Radha Krishna

doi:10.1016/j.jenvman.2018.06.033

Cited by 137 publications

(65 citation statements)

References 29 publications

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“…There are many types of learning algorithms available to derive the input-output relationships. The most commonly used algorithm is the learning algorithms for feed forward back propagation [57][58][59]. The widely used nonlinear activation function is sigmoid function whose output lies between 0 and 1, and the sigmoid transfer function is given by:…”

Section: Multi-layer Feed Forward Neural Network (Mlffnn)mentioning

confidence: 99%

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Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Ahmad¹,

Ghritlahre²,

Chandrakar³

2020

Tr Ren Energy

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Section: Multi-layer Feed Forward Neural Network (Mlffnn)mentioning

confidence: 99%

“…The hidden layer of RBF model is Radial basis activation function. The hidden layer's transfer function is normally a Gaussian function, which is expressed as [60,62]…”

Section: Radial Basis Function (Rbf)mentioning

confidence: 99%

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Ahmad¹,

Ghritlahre²,

Chandrakar³

2020

Tr Ren Energy

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show abstract

“…Some of the important applications of solar energy are listed below [2,[16][17][18][19][20] Fig.1 In particular, the direct conversion of sunlight into electricity by solar photovoltaic (PV) technology possesses great potential and represents a technically viable and sustainable solution for energy demands. Solar energy is an essential energy of all non-conventional sources.…”

Section: Applications Of Solar Energymentioning

confidence: 99%

Economic analysis and Case Study on Solar On-Grid System at Mahanadi Bhawan, Mantralaya, Naya Raipur

Suriyakumar

Chandrakar

Verma

et al. 2020

RJET

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CREDA has installed the On-Grid Solar power plant of 1.1MWp capacity at capital complex Naya Raipur (C.G.) INDIA. Economic analysis has been done at Mantralaya buildings, regarding generation of electricity from solar power plant, electricity exported to grid, electricity utilized in mantralaya buildings, electricity imported from CSEB in KWh, amount of imported power as electricity bill, rebate for exported power in Rs. (As per electricity bill) and the total payable amount. As more attention is being focused on the development of renewable energy resources widely in Chhattisgarh state, technical and economic assessment has been done. These assessments are based on the field and the actual performances of the renewable energy conversion system. This study represents the economic analysis of 1.1MWp Capacity installed at capital complex Naya Raipur.

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“…18 The GRNN is composed of the input layer, pattern layer, summation layer, and the output layer. [19][20][21] Using pattern layer Gaussian function operation we can create output that is near nonlinear. The GRNN framework is shown in Figure 3.…”

Section: Grnnmentioning

confidence: 99%

Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system

Tian

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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As product quality and production capacity requirements for precision processing become higher, making machine tools smart and improving performance is becoming the trend. In terms of machine tool processing quality, tool wear and chatter vibration are the factors that most directly affect processing quality. Traditionally, operators rely on their experience in determining whether the tool is worn or if there is chatter vibration. However, experience is not quantified data. Experience does not have a uniform judgment standard and can easily lead to wrong judgment. In recent years, many scholars have conducted in-depth studies of the two aforementioned phenomenons. These studies mainly use time and frequency domains to search for phenomenon features as the determination basis. Of time and frequency domain, studies on frequency domain are the most common. However, the frequency domain method requires a large amount of calculation, and the analysis process requires an excessive quantity of data dimensions, making this method unsuitable for real-time analysis. Thus, we propose a general regression neural network analysis method based on Chua's circuit and a fractional-order Lorenz master/slave composite chaotic system for detecting lathe tool turning chatter vibration in this study. We compared the dynamic error features produced by various fractionalorder chaotic systems and chose fractional orders with more obvious feature changes. We then substituted general regression neural network categorization. Compared to the frequency domain analysis method, the method proposed in this study requires fewer data dimensions, fewer calculations, and higher efficiency. Our proposed method also has higher precision and a higher discrimination rate. The result of this study shows that our proposed method has a 100% discrimination rate for determining turning chatter vibration.

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Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique

Cited by 137 publications

References 29 publications

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Economic analysis and Case Study on Solar On-Grid System at Mahanadi Bhawan, Mantralaya, Naya Raipur

Lathe tool chatter vibration diagnostic using general regression neural network based on Chua’s circuit and fractional-order Lorenz master/slave chaotic system

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