Modeling of Basil Leaves Drying by GA–ANN

Taheri‐Garavand, Amin; Rafiee, Shahin; Keyhani, Alireza; Javadikia, Payam

doi:10.1515/ijfe-2012-0224

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The effect of the interaction between factors is also neglected, leading to a low efficiency in process optimization, which is not guaranteed to find the optimal value [ 19 ]. Works by [ 20 , 21 ] have successfully optimized the network setting such as the number of neurons, the epoch and training function using the genetic algorithm (GA), and differential evaluation (DE). There is no specific rule used in selecting the value of network parameters, and this process is dependent on the complexity of the modelled system.…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction

Ibrahim

Wahab

2022

Membranes

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This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error.

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Section: Introductionmentioning

confidence: 99%

Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction

Ibrahim

Wahab

2022

Membranes

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“…In recent times, many researchers have used response surface methodology [ 31 – 33 ] and genetic algorithms [ 34 , 35 ] to optimise ANN topology.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of ANN topology for predicting the rehydrated apple cubes colour change using RSM and GA

Winiczenko

Górnicki

Kaleta

et al. 2016

Neural Comput & Applic

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In this study, an efficient optimisation method by combining response surface methodology (RSM) and genetic algorithm (GA) is introduced to find the optimal topology of artificial neural networks (ANNs) for predicting colour changes in rehydrated apple cubes. A multi-layered feed-forward backpropagation ANN model of algorithms was developed to correlate one output (colour change) to four input variables (drying air temperature, drying air velocity, temperature of distilled water and rehydration time). A predictive model for ANN topology in terms of the best mean squared error (MSE) performance on validation samples was created using RSM. RSM model was integrated with an effective GA to find the optimum topology of ANN. The optimum ANN had minimum MSE when the number of hidden neurons, learning rate, momentum constant, number of epochs and number of training runs were 13, 0.33, 0.89, 3869 and 3, respectively. MSE of optimal ANN topology on validation samples was 0.0072095. It turned out that the optimal ANN topology can be considered as more precise for predicting colour change in the rehydrated apple cubes. Mean absolute error and regression coefficient (R) of the optimal ANN topology were determined as 0.0259 and 0.96475 for training, 0.0399 and 0.95243 for testing and 0.0264 and 0.95151 for validation data sets. The results of the testing model on new samples showed excellent agreement between the actual and predicted data with coefficient of determination R 2 = 0.97.

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