Potential of particle swarm optimization based radial basis function network to predict the discharge coefficient of a modified triangular side weir

Zaji, Amir Hossein; Bonakdari, Hossein; Shamshirband, Shahaboddin; Qasem, Sultan Noman

doi:10.1016/j.flowmeasinst.2015.06.007

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…Because of the simplicity and flexibility, the RBNN method (Broomhead and Lowe 1988) is frequently used in various fields of the hydraulic engineering problems (Gan et al 2012;Zaji et al 2015;Al-Abadi 2016). As shown in Fig.…”

Section: Radial Basis Neural Networkmentioning

confidence: 99%

An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

et al. 2019

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In this study, a comparative analysis is done to evaluate the ability of classified radial basis function neural network (CRB-FNN) model in estimation of flow variables in sharp open-channel bends with bend angles of 60° and 90°. Accordingly, a RBFNN model is integrated with classification method to design a novel CRBFNN model to predict two velocity and flow depth parameters in a 60° sharp bend. Furthermore, Gholami et al. (Neural Comput Appl 30:1-15, 2018a) pointed out to acceptable ability and more efficiency improvement of hybrid CRBFNN model in prediction of flow variables in 90° sharp open-channel bend compared to simple RBF model. On the other hand, the flow pattern in sharp bends is more complicated than in mild open-channel bends. Moreover, the behavior of flow and its variables is varied in 60° and 90° sharp bends. Therefore, the present paper is aimed to evaluate the performance of RBF and CRBF models in two 60° and 90° (Gholami et al. 2018a) sharp open-channel bends. Available experimental data for velocity and flow depth at six different hydraulic conditions are used to train and test the CRBFNN and simple radial basis function neural network (RBFNN) networks in 60° open-channel bend. Accordingly, efficiency of both RBFNN and CRBFNN models in different bend cross sections is evaluated and compared with each other. The results show that using classified model has improved the simple RBF model performance, as in the CRBFNN model, the error root mean square error and mean absolute error value, 18% and 15.3% for the flow depth prediction and 9% and 5% for the velocity prediction compared to the simple RBFNN model is reduced, respectively. Furthermore, the comparison of model performance in 60° and 90° bends represents that both RBFNN and CRBFNN models in all discharge values in velocity prediction have more ability in 60° bend so that the mean absolute relative error (MARE) value in 60° bend is equal to 0.080 and 0.082 which are lower than MARE values in 90° bend (0.125 and 0.131 for RBFNN and CRBFNN, respectively). Furthermore, both RBFNN and CRBFNN models with lower MARE values equal to 0.015 and 0.012 in 90° bend have more accuracy than the models in 60° bend (0.017 and 0.014). Therefore, the proposed classified models can be used in design and implementation of the curved channels with various bend angles.

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Section: Radial Basis Neural Networkmentioning

confidence: 99%

An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

et al. 2019

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“…For f 6 , it is inevitable for the classi- cal mathematical approximation algorithm to be ineffective. Both SA-CPSO and LDIW-PSO have demonstrated excellent convergence results in f 6 , a discontinuous staircase function.…”

Section: Special Functionmentioning

confidence: 99%

“…These two important strengths have decided its theoretical value in scientific research and practical significance in engineering applications. PSO was first proposed by Kennedy and Eberhart in 1995 inspired by the study on the foraging behaviors of birds [6]. When the bird flocks, the most simple and effective strategy is to search the surrounding areas of the bird closest to food.…”

Section: Introductionmentioning

confidence: 99%

A cooperative particle swarm optimization with constriction factor based on simulated annealing

Zhang

Wang

2018

Computing

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As many engineering optimization problems are rather complicated, it is usually necessary to search the optimal solution in a complex and huge search space. When faced with these large-scale problems, conventional optimization algorithms need to traverse the entire search space and it is impossible for them to finish the search within polynomial time. Moreover, it can't meet requirements in terms of computation velocity, convergence and sensitivity to initial value. So, it is very difficult to apply them to engineering optimization problems. Swarm intelligence methods simulate the collective behaviors of social creatures in the nature and they come from the relationship between the community formed by simple individuals and the environment as well as the interactions between the individuals. A single individual can only perform simple tasks, but the population formed by single individuals can fulfill complex tasks. Such intelligence presented by such population is called swarm intelligence. Due to the limitations of existing optimization algorithms, it is usually impractical to obtain excellent computational performance with only one optimization algorithm. In consideration of the jumping property of simulated annealing, it is not easy to get trapped into local minimum and it has strong local search capability near the optimal value and fast convergence velocity. This paper combines it with particle swarm optimization, proposes a cooperative particle swarm optimization with constriction factor based on simulated annealing (SA-CPSO), offers guidelines on selection of related parameters and dynamically adjusts the particle velocity according to its movement track. In this way, it improves the convergence velocity of the algorithm by improving the spatial search ability of the particle so as to make the particle accept the solution which makes the fitness of the objective function "better" as well as the solution that makes the said fitness "worse" at a certain probability during the flight of the particle. The experiment shows that the SA-CPSO improves the diversity of the particle and enhances its ability to get rid of locally optimal solutions. So, SA-CPSO is not easy to be trapped into local optimum and it has stronger ability of global optimization, a faster convergence velocity and higher convergence accuracy.

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“…The authors concluded that the RBNN model has higher performance compare with the other regression methods in simulating the considered side weir discharge coefficient. Zaji et al (2015) simulated the present side weir discharge coefficient using the PSO based RBNN method. The authors concluded that the PSO algorithm successfully improved the RBNN performance.…”

Section: Introductionmentioning

confidence: 99%

New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

Bonakdari

Zaji

2018

Appl Water Sci

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In many hydraulic structures, side weirs have a critical role. Accurately predicting the discharge coefficient is one of the most important stages in the side weir design process. In the present paper, a new high efficient side weir is investigated. To simulate the discharge coefficient of these side weirs, three novel soft computing methods are used. The process includes modeling the discharge coefficient with the hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) and three optimization algorithms, namely Differential Evaluation (ANFIS-DE), Genetic Algorithm (ANFIS-GA) and Particle Swarm Optimization (ANFIS-PSO). In addition, sensitivity analysis is done to find the most efficient input variables for modeling the discharge coefficient of these types of side weirs. According to the results, the ANFIS method has higher performance when using simpler input variables. In addition, the ANFIS-DE with RMSE of 0.077 has higher performance than the ANFIS-GA and ANFIS-PSO methods with RMSE of 0.079 and 0.096, respectively.

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Potential of particle swarm optimization based radial basis function network to predict the discharge coefficient of a modified triangular side weir

Cited by 16 publications

References 22 publications

An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

A cooperative particle swarm optimization with constriction factor based on simulated annealing

New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

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