Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

Mohebbi, Ali

doi:10.1016/j.jhazmat.2007.12.107

Cited by 26 publications

(11 citation statements)

References 23 publications

(45 reference statements)

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“…Karzynski et al [7] used GAs to optimise both the architecture and the weights of ANN for multiclass microarray classification. Taheri and Mohebbi [15] used the GA to fine-tune ANN parameters, including the number of nodes in the hidden layers, the momentum and the learning rates. Zorić and Pandžić [17] utilised the GA to find the near optimal ANN topology in the real-time lip synchronisation classification.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Tong

Mintram

2010

Int. J. Mach. Learn. & Cyber.

126

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Hybrid genetic algorithms (GA) and artificial neural networks (ANN) are not new in the machine learning culture. Such hybrid systems have been shown to be very successful in classification and prediction problems. However, little attention has been focused on this architecture as a feature selection method and the consequent significance of the ANN activation function and the number of GA evaluations on the feature selection performance. The activation function is one of the core components of the ANN architecture and influences the learning and generalization capability of the network. Meanwhile the GA searches for an optimal ANN classifier given a set of chromosomes selected from those available. The objective of the GA is to combine the search for optimum chromosome choices with that of finding an optimum classifier for each choice. The process operates as a form of co-evolution with the eventual objective of finding an optimum chromosome selection rather than an optimum classifier. The selection of an optimum chromosome set is referred to in this paper as feature selection. Quantitative comparisons of four of the most commonly used ANN activation functions against ten GA evaluation step counts and three population sizes are presented. These studies employ four data sets with high dimension and low significant datum instances. That is to say that each datum has a high attribute count and the unusual or abnormal data are sparse within the data set. Results suggest that the hyperbolic tangent (tanh) activation function outperforms other common activation functions by extracting a smaller, but more significant feature set. Furthermore, it was found that fitness evaluation sizes ranging from 20,000 to 40,000 within populations ranging from 200 to 300, deliver optimum feature selection capability. Again, optimum in this sense meaning a smaller but more significant feature set.

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Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Tong

Mintram

2010

Int. J. Mach. Learn. & Cyber.

126

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“…The coding length was set to 30. Each chromosome consisted of a piece of binary code of the connection weight and threshold, which are generally limited in the search space [21].…”

Section: Initial Structure Optimization By the Genetic Algorithmmentioning

confidence: 99%

Dynamic Prediction and Optimization of Energy Efficiency Operational Index (EEOI) for an Operating Ship in Varying Environments

Sun

Wang

Liu³

et al. 2019

JMSE

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The demands for lower Energy Efficiency Operational Index (EEOI) reflect the requirements of international conventions for green shipping. Within this context it is believed that practical solutions for the dynamic optimization of a ship's main engine and the reduction of EEOI in real conditions are useful in terms of improving sustainable shipping operations. In this paper, we introduce a model for dynamic optimization of the main engine that can improve fuel efficiency and decrease EEOI. The model considers as input environmental factors that influence overall ship dynamics (e.g., wind speed, wind direction, wave height, water flow speed) and engine revolutions. Fuel consumption rate and ship speed are taken as outputs. Consequently, a genetic algorithm is applied to optimize the initial connection weight and threshold of nodes of a neural network (NN) that is used to predict fuel consumption rate and ship speed. Navigation data from the training ship "YUMING" are applied to train the network. The genetic algorithm is used to optimize engine revolution and obtain the lowest EEOI. Results show that the optimization method proposed may assist with the prediction of lower EEOI in different environmental conditions and operational speed.Harilaos N. Psarafits et al. stated that the ship speed is a decision variable for fuel consumption and emissions [5]. In fact, most methods applied to improve ship efficiency are to adjust ship speed. Route and speed optimization are operational procedures that may be used to improve shipping efficiency and EEOI. Inge Norstad et al. used a recursive smoothing algorithm to optimize the speed for ship routing and scheduling for a tramp ship [6]. Wang Shuai'an et al. conducted an in-depth analysis on the relationship between fuel consumption and ship speed based on the historical operational data of a container liner and optimized the ship speed by using nonlinear programming [7]. Ming-Chung Fang et al. applied a heuristic method to optimize ship routing in different weather conditions [8].Lower steaming is another kind of validated method for decreasing fuel consumption and the EEOI. However, such operational strategy is limited by many factors, including the ETA (estimated time of arrival), fuel price, charter rates, influence of speed reduction on engine efficiency, and so forth [9,10].Joan P. Peterse developed a fuel consumption model by way of a machine learning method and ship navigation data [11]. Benjamin applied a neural network (NN) trained by noon report data to establish a propulsion power model under hydrostatic conditions [12]. Wang Kai et al. used the wavelet packet neural network to predict the sea conditions for a short-journey of a sailing ship and introduced a real-time energy efficiency model under predicted sea conditions [13].This paper suggests a method that could be used to identify lower EEOI using real time operational data. An EEOI during a fixed time span is introduced to indicate fuel efficiency. A back propagation neural network, which is trained by navig...

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“…In 2008, Mahboobeh Taheri clarified the possibility of GA‐ANN improving the efficiency of neural network algorithms by predicting the collection efficiency of venturi scrubbers. This algorithm is characterized by the use of the GA algorithm to determine the number of neurons, momentum, and learning rate in the hidden layer, thereby minimizing the time required to find the optimal architecture and parameters for ANN‐based backpropagation …”

Section: Neural Network Algorithm Efficiencymentioning

confidence: 99%

Application of artificial neural networks to X‐ray fluorescence spectrum analysis

et al. 2018

X-Ray Spectrometry

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X‐ray fluorescence (XRF) is widely applied as a mature nondestructive testing method, and appropriate improvement of quantitative analysis methods can improve the accuracy of XRF. Artificial neural network is an intelligent information processing system, its developments and application in XRF are reviewed, and representative models (back propagation, radial basis function, genetic algorithm artificial neural network, and others) are discussed in more details in overfitting, generalization, and algorithm efficiency. Potential directions of developing artificial neural network applied in XRF are proposed in this review as a further study.

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Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

Cited by 26 publications

References 23 publications

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

Dynamic Prediction and Optimization of Energy Efficiency Operational Index (EEOI) for an Operating Ship in Varying Environments

Application of artificial neural networks to X‐ray fluorescence spectrum analysis

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