Elman Neural Network Soft-Sensor Model of Conversion Velocity in Polymerization Process Optimized by Chaos Whale Optimization Algorithm

Sun, Weiyuan; Wang, Jie-Sheng

doi:10.1109/access.2017.2723610

Cited by 50 publications

(31 citation statements)

References 20 publications

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“…The literature [29] proposed a chaos WOA (CWOA) algorithm to calculate and automatically adjust the internal parameters of the optimization algorithm through chaotic mapping. This idea is essentially different from the literature [21]. The experimental results show Symmetry 2018, 10, 210 3 of 31 that CWOA can effectively optimize the parameters of photovoltaic cells and their components.…”

Section: Introductionmentioning

confidence: 84%

“…In terms of parameter optimization, the literature [20] uses WOA to optimize two parameters of a least-squares support vector machine to establish a WOA-LSSVM (WOA-least-squares support vector machine) model to predict carbon dioxide emissions. Similarly, chaos WOA algorithm (CWOA) is proposed to optimize the parameters of Elman neural network in the literature [21], so as to establish a soft measurement model and predict variables. The literature [22] compares WOA with other methods for optimizing neural network parameters quantitatively and qualitatively.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

2018

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Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA's unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

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Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

2018

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“…WOA has been hybridized with the various machine learning algorithms like SVM, ANN, etc. [9][10][11][12]. WOA consists of the following two phases [7].…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

Texture classification using convolutional neural network optimized with whale optimization algorithm

et al. 2019

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Texture classification is an active area of research in the field of pattern recognition. Convolutional neural networks (CNNs) have a remarkable capability of recognizing patterns and are one of the most efficient deep learning techniques. But, finding the optimal values of the different hyperparameters of the CNN is a major challenge. Nature-inspired algorithms (NIAs) are the meta-heuristic algorithms well-known for their optimizing capability. Whale optimization algorithm (WOA) is a recent nature-inspired algorithm (NIA) that is inspired by the hunting behaviour of the humpback whales. In this paper, we propose a novel deep learning technique for texture recognition using a CNN optimized through WOA. We apply WOA at the two different levels in the CNN: In the convolutional layer (for optimizing the values of the filters), and in the fully-connected layer (for optimizing the values of the weights and biases). For examining the performance of our technique, we apply it to the following three benchmark texture datasets: Kylberg v1.0, Brodatz, and Outex_TC_00012. Our model performs better than the most of the existing methods for the Kylberg and the Outex_TC_00012 datasets and gives competitive results for the Brodatz dataset. It is evident from the results that our model has the potential for application in the field of texture recognition.

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“…For example, the self-feedback gain coefficient of ENN is generally determined by the attempt, which leads to low learning efficiency [31]. Fortunately, along with the development of softcomputing technique, abundant optimization algorithms have emerged to improve the deficiency of single-objective neural network in predicting, such as genetic algorithm (GA), particle swarm optimization (PSO), improved particle swarm optimization (IPSO), and whale optimization algorithm (WOA) [32][33][34][35]. Although the aforementioned optimization algorithms can improve the prediction performance, they still have some defects of slow convergence speed, local optimum, and long training process.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Zhou

et al. 2019

Journal of Energy

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As a pillar industry of national economy, China’s construction industry is still facing the status of substantial energy consumption and high CO2 emissions, which is a key field of energy conservation and emission reduction. In CO2 emissions research, it is essential to focus on analyzing the present and future trends of CO2 emissions in China’s construction industry. This article introduces a novel prediction model, in which the weighted algorithm is combined with Elman neural network (ENN) optimized by Adaptive Boosting algorithm (Adaboost) for evaluating future CO2 emissions in China’s construction industry. Firstly, logarithmic mean Divisia index (LMDI) is used to decompose CO2 emissions into economy, structural, intensity, and population indicators, posing as inputs to the weighted Adaboost-ENN model. Then, through comparison with other three models based on the data of total CO2 emissions in China’s construction industry during 2004-2016, there is evidence that the proposed model makes a favorable prediction performance. On this basis, we employ scenario analysis to predict future trend of CO2 emissions in China’s construction industry. It can be found that the peak of CO2 emissions in China’s construction industry will be achieved before 2030 in high carbon scenario (HS) and baseline carbon scenario (BS), whereas it will not be realized in low carbon scenario (LS). Finally, the specific policy recommendations related to energy conservation and emission reduction in China’s construction industry are proposed.

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Elman Neural Network Soft-Sensor Model of Conversion Velocity in Polymerization Process Optimized by Chaos Whale Optimization Algorithm

Cited by 50 publications

References 20 publications

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Texture classification using convolutional neural network optimized with whale optimization algorithm

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

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Elman Neural Network Soft-Sensor Model of Conversion Velocity in Polymerization Process Optimized by Chaos Whale Optimization Algorithm

Cited by 50 publications

References 20 publications

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Texture classification using convolutional neural network optimized with whale optimization algorithm

Forecasting CO2 Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

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Product

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Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis