TY - JOUR ID - 66288 TI - Modeling for System Optimization with Small Dataset Using Neural Network JO - Advances in Industrial Engineering JA - AIE LA - en SN - AU - Hassanpour, Hamid AU - Alyannezhadi, Mohammad Mahdi AD - Department of Image Processing and Data Mining Lab, Shahrood University of Technology, Semnan, Iran Y1 - 2018 PY - 2018 VL - 52 IS - 1 SP - 25 EP - 35 KW - System modeling KW - Optimization KW - Multi-layer neural network KW - Genetic algorithm DO - 10.22059/jieng.2018.232557.1371 N2 - The shortage of data is one of the most important problems in system modeling and optimization in industrial applications. Typical modeling techniques are unable to properly model a system with a limited dataset. In this paper, a modeling method for optimization of these systems is proposed. The proposed method has two main steps. In the first step, the model is employed to generate data using neural network. This model determines the correspondence input of each output. In the second step, optimization of the generated model is performed using genetic algorithm. Inputs leading to the specified output can be estimated using the proposed system. Optimality of the system can be explained by an evaluation function. The proposed method was evaluated in two different experiments on a time series and a real data. Results of the experiments were analyzed using mean square error. The experimental results show the capability of the proposed method in system modeling and optimization. UR - https://aie.ut.ac.ir/article_66288.html L1 - https://aie.ut.ac.ir/article_66288_d0727c25b8153b7c01c83502ef64d4f7.pdf ER -