Application of a GA-Optimized NNARX controller to nonlinear chemical and biochemical processes

Medi, Bijan; Asadbeigi, Ayyob

doi:10.1016/j.heliyon.2021.e07846

Cited by 5 publications

References 28 publications

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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Kaveh

Mesgari

2022

Neural Process Lett

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The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.

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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Kaveh

Mesgari

2022

Neural Process Lett

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Wind turbine electrohydraulic transmission system control for maximum power tracking with pump fault

Kumar

Venkaiah

Sarkar

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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The wind power generation system plays a significant role in the power sector as it is an environment-friendly green power system, increasing power demand, and technological development in wind power systems. Wind turbine systems are exposed to the harsh environment with continuous variation of wind speed with gusts causing damage and failure in system components along with the fluctuation of generated power. The hydrostatic transmission system has become one of the promising solutions over the gear transmission system for transmitting power from the turbine rotor to the generator. Further to reduce power generation costs in wind power systems, a suitable control system with parametric uncertainty and system fault plays a significant role. In this study, the 5 MW wind turbine model has been developed with the combination of blade element momentum theory and the electrohydraulic transmission system model. Moreover, the wind turbine system model has been imposed fault in the pump of electrohydraulic transmission system. The proposed wind turbine system model has been validated with the existing result. The blade element momentum theory has been used to estimate the optimum pump turbine couple rotational speed for maximum power tracking. Double loop controller has been used for wind turbine power transmission system control. The first controller loop has been used for pump and wind turbine system speed control for maximum power tracking, as a passive fault tolerance controller and the second control loop for motor and generator system speed control to regulate the frequency of the generated power. Interval type 2-fuzzy proportional–integral–derivative controller are suitable for high degree of uncertain system like wind power system due to their footprint of uncertainties. Proper choice of footprint of uncertainty provides robust performance against uncertainties and dynamic performance. Hence, the primary and secondary controller has been developed as interval type 2-fuzzy proportional–integral–derivative with inertial weight local search–based teaching–learning-based optimization controller. The inertial weight local search–based teaching–learning-based optimization interval type 2-fuzzy proportional–integral–derivative controller performance has been studied with benchmark sinusoidal test signals. The proposed inertial weight local search–based teaching–learning-based optimization interval type 2-fuzzy proportional–integral–derivative controller performance has been also compared with conventional proportional–integral–derivative and interval type 2-fuzzy proportional–integral–derivative controller. The proposed system performance has been compared with contemporary reported digital hydrostatic transmission wind turbine system and recently reported controller with consideration of fault in the pump. The proposed inertial weight local search–based teaching–learning-based optimization interval type 2-fuzzy proportional–integral–derivative controller performance has been compared through integral absolute error with interval type 2-fuzzy proportional–integral–derivative controller and recently reported proportional–integral–derivative sliding mode controller obtained as 0.0016, 0.0029, and 0.0031, respectively.

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A nonlinear autoregressive exogenous neural network (NARX-NN) model for the prediction of solvent-based oil extraction from Hura crepitans seeds

Ajala

Oyelade

Oke

et al. 2023

Chemical Product and Process Modeling

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Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds.

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Application of a GA-Optimized NNARX controller to nonlinear chemical and biochemical processes

Cited by 5 publications

References 28 publications

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Wind turbine electrohydraulic transmission system control for maximum power tracking with pump fault

A nonlinear autoregressive exogenous neural network (NARX-NN) model for the prediction of solvent-based oil extraction from Hura crepitans seeds

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