List of articles (by subject) artificial intelligence
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Open Access Article
1 - Comparative Study of LS-SVM, RVM and ELM for Modelling of Electro-Discharge Coating Process
Morteza Taheri Nader Mollayi Seyyed Amin Seyyedbarzani Abolfazl Foorginejad Vahide BabaiyanThe Electro-discharge coating process is an efficient method for improvement of the surface quality of the parts used in molds. In this process, Material Transfer Rate (MTR), an average Layer Thickness (LT) are important factors, and tuning the input process parameters MoreThe Electro-discharge coating process is an efficient method for improvement of the surface quality of the parts used in molds. In this process, Material Transfer Rate (MTR), an average Layer Thickness (LT) are important factors, and tuning the input process parameters to obtain the desired value of them is a crucial issue. Due to the wide range of the input parameters and nonlinearity of this system, the establishment of a mathematical model is a complicated mathematical problem. Although many efforts have been made to model this process, research is still ongoing to improve the modeling of this process. To this end, in the present study, three powerful machine learning algorithms, namely, Relevance Vector Machine (RVM), Extreme Learning Machine (ELM) and the Least Squares Support Vector Machine (LS-SVM) that have not been used to model this process, have been used. The values R2 above 0.99 for the training data and above 0.97 for the test data show the high accuracy and generalization capability degree related to the LS-SVM models, which can be applied for the input parameters tuning in order to attain a preferred value of the outputs. Manuscript profile -
Open Access Article
2 - Optimal Routing of Rocket Motion using Genetic Algorithm and Particle Swarm Optimization
Reza Tarighi M.H. Kazemi mohammad hosein khalesiIn this paper, a new approach to the use of genetic algorithms and the predictive control method, for goal tracking is presented. A hypothetical rocket is modelled for the analyses. Rocket guidance algorithm is developed to achieve a desired mission goal according to so MoreIn this paper, a new approach to the use of genetic algorithms and the predictive control method, for goal tracking is presented. A hypothetical rocket is modelled for the analyses. Rocket guidance algorithm is developed to achieve a desired mission goal according to some performance criteria and the imposed constraints. Given that goals can be fixed or moving, we have focused and expanded on this issue in this study and also the dynamic modelling of flying objects with six-degrees-of-freedom (DOF) is used to make the design more similar to the actual model. The predictive control method is used to predict the next step of rocket and aim movement. At each step of the problem, the rocket distance to the aim is obtained, and a trajectory is predicted to move the rocket towards the purpose. The objective function of this problem, in addition to the distance from the rocket position to the target, are also parameters of the dynamic model of the rocket. Therefore, these parameters are optimized at each step of the problem solving. Ultimately, the rocket strikes the intended aim by following this optimal path. Finally, for the validation of the model, numerical results are obtained for both Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Simulation results demonstrate the effectiveness and feasibility of the proposed optimization technique. Manuscript profile -
Open Access Article
3 - Evaluation and Comparison of Different Artificial Neural Networks and Genetic Algorithm in Analyzing a 60 MW Combined Heat and Power Cycle
parisa ghorbani Arash KarimipourThe constant growth of energy consumption, increased fuel costs, non-renewable fossil fuel sources, and environmental pollution caused by increased emission of greenhouse gases, and global warming highlight the need for the analysis and optimization of main energy gener MoreThe constant growth of energy consumption, increased fuel costs, non-renewable fossil fuel sources, and environmental pollution caused by increased emission of greenhouse gases, and global warming highlight the need for the analysis and optimization of main energy generation bases, i.e. power plants. The Artificial Neural Network (ANN) is a useful novel method for better processing information and controlling, and optimizing and modeling industrial processes. For the first time in this study, an ANN was designed and applied to data extracted from modeling and analyzing a 60 MW combined heat and power generation power plant. To this end, the error backpropagation network was selected as the optimal network, and the generator load or capacity, condenser pressure, and Feedwater temperature were considered inputs to the ANN. The energy and exergy efficiencies of the power plant and the overall energy and exergy losses of the cycle were considered outputs of the ANN. The ANN was coded and designed with the help of MATLAB. The Genetic Algorithm (GA) was used to obtain the optimal values of input parameters and the minimum losses and maximum efficiencies based on the first and second laws of thermodynamics. Manuscript profile -
Open Access Article
4 - Multi-Objective Optimization of Plate Heat Exchangers by Employing an Imperialist Competitive Algorithm
Mohammadjavad Mahmoodabadi Soodeh ZarnegarIn this paper, the multi-objective optimum design of plate fin heat exchangers is investigated. To this end, the efficiency and cost as two important factors for the design of heat exchangers are regarded as the objective functions. Fin pitch, fin height, fin offset len MoreIn this paper, the multi-objective optimum design of plate fin heat exchangers is investigated. To this end, the efficiency and cost as two important factors for the design of heat exchangers are regarded as the objective functions. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length and hot stream flow length are considered as six design parameters. The ε-NTU method is applied to estimate the heat exchanger pressure drop and its effectiveness. A case study related to a gas furnace in Barez tire group located in the northwest of Kerman, Iran is considered for the constant parameters. The Imperialist Competitive Algorithm (ICA) is used to find the optimal design parameters to achieve the maximum thermal efficiency and minimum consumption cost. The method of the weighting coefficients is applied to change the considered multi-objective optimization problem as a single objective one. Furthermore, the effects of variations of the design parameters on the objective functions are independently investigated, and the related graphs are presented. Manuscript profile