Modeling for system optimization with small dataset by neural network

The shortage of data is one of the most important problem in system modeling and optimization in applications such as industry and agriculture. In this paper, a modeling method for optimization of these systems is proposed. The proposed method has two main steps. I) designing a model to generate data by using neural network .This model determines the correspondence input of each output. Usually, state of the system can be explained by some features. These features are system parameters. II) optimization of the generated model using genetic algorithm. Optimality of the system can be explained by evaluation function. The proposed method evaluated by two experiments on an artificial time series and a real data. The experimental results show the capability of the proposed method in system modeling and optimization.