The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review

Maarouf, Mustapha; Sosa, Adriel; Galván, Blas; Greiner, David; Winter, Gabriel; Méndez, Máximo; Aguasca, Ricardo

doi:10.1007/978-3-319-11541-2_4

Cited by 11 publications

(4 citation statements)

References 130 publications

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“…After the network is trained, the output can be predicted within few seconds. ANN-based models are still being applied successfully to overcome engineering problems in different fields such as adaptive control, pattern recognition, robotics, image processing, medical diagnostics, fault detection, process monitoring, renewable and sustainable energy, laser applications and nonlinear system identification [10]- [17].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Al-Dunainawi¹,

Abbod²

2016

ijarai

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Abstract-Compositions measurement is a vitally critical issue for the modelling and control of distillation process. The product compositions of distillation columns are traditionally measured using indirect techniques via inferring tray compositions from its temperature or by using an online analyser. These techniques were reported as inefficient and relatively slow methods. In this paper, an alternative procedure is presented to predict the compositions of a binary distillation column. Particle swarm optimisation based artificial neural network PSO-ANN is trained by different algorithms and tested by new unseen data to check the generality of the proposed method. Particle swarm optimisation is utilised, here, to choose the optimal topology of the network. The simulation results have indicated a reasonable accuracy of prediction with a minimal error between the predicted and simulated data of the column.

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Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Al-Dunainawi¹,

Abbod²

2016

ijarai

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“…As shown in Figure 2, the radial basis function (RBF) is a two-layer feed-forward network with a single hidden layer [24], which can approximate the nonlinear function with arbitrary precision and realize the global optimization in theory [25]. It is a direct mapping from the input layer to the hidden layer.…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model

2022

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Owning to the advantage of keeping the operating environment safe, high reliability, and low production cost, predictive maintenance has been widely used in industry and academia. Predictive maintenance based on degeneration state mainly studies the degeneration prediction. However, on account of the error of the sensor and human, condition monitoring data may not directly reflect the true degeneration. The degeneration model with dynamic explanatory covariates which is named as proportional hazard model is proposed to deal with the semi-observed monitoring condition. And the degeneration prediction mainly adopts a single prediction model, which leads to low prediction accuracy. A combination forecasting model can effectively solve the above problem. Compared to the traditional prediction method, the neural network model can use the “black box” characteristic to indirectly construct the degeneration model without complex mathematical derivation. Therefore, we propose a combination BP-RBF-GRNN neural network model which is applied to improve the degeneration prediction with dynamic covariate. Based on the above two aspects, a predictive maintenance optimization framework based on the proportional hazard model and BP-RBF-GRNN neural network model is proposed to improve maintenance efficiency and reduce maintenance costs. The simulation results of thrust ball bearing show that the proposed method can effectively improve the degeneration prediction accuracy and reduce the maintenance cost rate to a certain extent.

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“…Once the network has learned, by introducing enough dataset of input-output pairs, the output can be estimated faster and with better efficiency. ANN-based approaches are still being applied extensively to overcome various complications in many diverse practical applications, ranging from nonlinear system identification to adaptive control, as well as pattern recognition, image processing, medical diagnostics, process monitoring, renewable and sustainable energy and laser-based applications [29]- [31].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems

Al-Dunainawi

Abbod

Jizany

2017

Engineering Applications of Artificial Intelligence

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The proposal of this study is a new nonlinear autoregressive moving average, NARMA-L2 controller, which is based on an adaptive neuro-fuzzy inference system, ANFIS architecture. The new control configuration employs Sugeno-type fuzzy inference system FIS submodels to map input characteristics to the output of a dynamic and nonlinear system. The default hybrid learning algorithm (Backpropagation and Least Square Error) has been carried out as well as particle swarm optimisation (PSO) approach, in order to select the optimal parameters of the ANFIS submodels. Once the system has been modelled efficiently and accurately, the proposed controller is designed by rearranging the generalised FIS submodels. The controller performance is evaluated by simulations conducted on a binary distillation column, which is characterised by a nonlinear and dynamic behaviour. The obtained results show that the PSO-ANFIS based NARMA-L2 achieved more efficient modelling and control performances when compared with other controllers. These controllers include ANN-based NARMA-L2, (PD, PI and PID like) fuzzy-tuned by GA and PSO and traditional PID, which are also implemented to the column for comparison. Stability and robustness of the proposed controller regarding system inputs variance have also been tested by applying asynchronous setpoints of both inputs of the process.

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The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review

Cited by 11 publications

References 130 publications

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model

A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems

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