the long term goal attaining of economic system management requires the establishment of a dynamic framework to integrate multiple perspectives. this article considers the problem of economic development in macro level. as economic systems are complex adaptive systems composed of numerous numbers of components of dynamic and multidimensional nature, dynamics method is applied in this research. ...

The problem of system identification is addressed by means of general neural networks with locally distributed dynamics. These networks are based on both multilayer perceptron and radial basis function structures. Evolutionary algorithms are suggested to select the optimal neural topologies and parameters. The accuracy of the neural models and the complexity of their architectures are evaluated...

This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as...

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in co...

Abstract The aim of the contribution is creating of SDOF and simple MDOF models with included complex information of civil structures for dynamic analyses. The dynamic parameters of models are obtained as inverse problem solution. The inverse problem is defined as an optimization problem. The objective is to minimize the error index between measured response of the real structure and calculated...

One of the well-known recurrent neural networks is the Elman network. Recently, it has been used in applications of system identification. The network has feedforward and feedback connections. It can be trained essentially as a feedforward network by means of the basic backpropagation algorithm, but its feedback connections have to be kept constant. For training success, it is important to sele...