Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

Wijayasekara, Dumidu; Manic, Milos; Sabharwall, Piyush; Utgikar, Vivek

doi:10.1016/j.nucengdes.2011.04.045

Cited by 41 publications

(15 citation statements)

References 22 publications

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“…According to Wijayasekara et al (2011), the chance of over-training a network increases with the number of neurons and the number of training epochs. Thus, as the number of neurons increases, the possibility of the network describing the training data pattern exactly also increases.…”

Section: Resultsmentioning

confidence: 99%

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Mariano

Paixão

Lima

et al. 2013

Animal

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Several researchers have developed prediction equations to estimate the metabolisable energy (ME) of energetic and protein concentrate feedstuffs used in diets for broilers. The ME is estimated by considering CP, ether extract, ash and fibre contents. However, the results obtained using traditional regression analysis methods have been inconsistent and new techniques can be used to obtain better estimate of the feedstuffs' energy value. The objective of this paper was to implement a multilayer perceptron network to estimate the nitrogen-corrected metabolisable energy (AMEn) values of the energetic and protein concentrate feeds, generally used by the poultry feed industry. The concentrate feeds were from plant origin. The dataset contains 568 experimental results, all from Brazil. This dataset was separated into two parts: one part with 454 data, which was used to train, and the other one with 114 data, which was used to evaluate the accuracy of each implemented network. The accuracy of the models was evaluated on the basis of their values of mean squared error, R 2 , mean absolute deviation, mean absolute percentage error and bias. The 7-5-3-1 model presented the highest accuracy of prediction. It was developed an Excel R AMEn calculator by using the best model, which provides a rapid and efficient way to predict the AMEn values of concentrate feedstuffs for broilers.Keywords: avian production, broilers, metabolisable energy, multilayer perceptron ImplicationsIt is difficult and expensive to estimate the nitrogencorrected metabolisable energy (AMEn) values of the feedstuffs used for broilers. The results of this study demonstrate that the implementation of multilayer perceptron networks (MLP) in a meta-analysis is suitable to estimate these energy values. Furthermore, a calculator was created on the basis of the results of the MLP, which allows an efficient way of predicting the AMEn values.

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Section: Resultsmentioning

confidence: 99%

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Mariano

Paixão

Lima

et al. 2013

Animal

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“…where w ij and x ij are the weight of connection and input signal number from input j to neuron I, respectively; w io is the threshold bias of i. The structure of ANN is illustrated in Figure 8 (adopted from [109]). There are a number of training algorithms used in building ANN such as Genetic Algorithm, incremental and batch back propagation, quick propagation, Broydene Fletcher Goldforbe Shanno Quasi network back propagation and many others [47].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

et al. 2019

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Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

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“…Tabel 3 menunjukkan umumnya semakin banyak node hidden layer MSE validitas menurun. Topologi jaringan dengan banyak node hidden layer menghasilkan kinerja lebih baik, namun untuk beberapa permasalahan, topologi jaringan dengan sedikit node hidden layer juga dapat memberikan kinerja yang baik (Wijayasekara et al, 2011). Kompleksitas node hidden layer berbeda pada setiap permasalahan tergantung dari pola data input dan output yang digunakan.…”

Section: Analisis Hubungan Total Fenol Dan Ph Terhadap Persentase Pemunclassified

Deteksi Pemalsuan Kopi Luwak Menggunakan Sifat Biolistrik Dan Jaringan Saraf Tiruan

Widyaningtyas

Sucipto

Hendrawan

2018

JTP

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Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

Cited by 41 publications

References 22 publications

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

Deteksi Pemalsuan Kopi Luwak Menggunakan Sifat Biolistrik Dan Jaringan Saraf Tiruan

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