Treffer: Visual Intelligent Modeling System Based on Hybrid Optimization Algorithms and Interactive Modeling Technology

Abstract: This study presents the design and implement of a visual intelligent modeling system by combinational application of genetic algorithm, wavelet analysis, artificial neural network model components and visual modeling technology to support interactive and fast modeling of the complex, non-linear, and dynamic process. To overcome local optima problems of ANN, a hybrid intelligent genetic neural network algorithms (HIGANN) has been established by recombining the strength of back propagation (BP) in weight learning, wavelet analysis in data preprocess and GA''s capability of global searching the architecture space. Meanwhile, to tackle the problems of low modeling efficiencies and complex programming design using conventional modeling technologies, based on an open modeling interface standard, an visual modeling tool with the good expansibility is established to facilitate the linkage and combination of different optimization models in a single environment with visual feedback and real-time interaction. In order to verify the feasibility and validity of the prototype system, a case study of runoff forecasting for some watershed located on the Qinjiang river basin, central China was carried out by using BP, HIGANN model separately. The results show that the visual intelligent modeling system can greatly enhance the easy-to-use capabilities and flexibility of interactive modeling and appeal in offering a positive prospect of improving the efficiency and robustness of current practice in hydrological modeling. [Copyright &y& Elsevier]