A novel neural network approach is presented for solving nonlinear bilevel programming problem. The proposed neural network is proved to be Lyapunov stable and capable of generating optimal solution to the nonlinear bilevel programming problem. The asymptotic properties of the neural network are analyzed and the condition for asymptotic stability, solution feasibility and solution optimality ar...

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...

Active control of sound and vibration has been the subject of a lot of research in recent years, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the s...

We consider a nonlinear nonconvex network flow problem that arises, for example, in natural gas or water transmission networks. Given is such network with active and passive components, that is, valves, compressors, pressure regulators (active) and pipelines (passive), and a desired amount of flow at certain specified entry and exit nodes of the network. Besides flow conservation constraints in...

Multi-layered neural networks have recently been proposed for nonlinear prediction and system modeling. Although proven successful for modeling time invariant nonlinear systems, the inability of neural networks to characterize temporal variability has so far been an obstacle in applying them to complicated non stationary signals, such as speech. In this paper we present a network architecture, ...

A new learning strategy is proposed for training of radial basis functions (RBF) network. We apply two different local optimization methods to update the output weights in training process, the gradient method and a combination of the gradient and Newton methods. Numerical results obtained in solving nonlinear integral equations show the excellent performance of the combined gradient method in ...

correct estimation of suspended sediment transported by a river is an important practice in water structure design, environmental problems and water quality issues. conventionally, sediment rating curve used for suspended sediment estimation in rivers. in this method discharge and sediment discharge or concentration related using regression relation that generally is exponential model. respect ...

artificial neural network is one of the most robust and reliable methods in online prediction of nonlinear incidents in machining. tool flank wear as a tool life criterion is an important task which is needed to be predicted during machining processes to establish an online tool life estimation system. in this study, an artificial neural network model was developed to predict the tool wear and ...

Non-linear dynamical systems are difficult to control due to the model uncertainties and external disturbances that may occur in these systems. This paper addresses the problem of identification using dynamic neural networks (DNNs) based on genetic algorithm (GA) for nonlinear dynamic systems. Four different dynamic neural networks are used for identification of the same nonlinear dynamic syste...