Neural networks, despite their empirically-proven abilities, have been little used for the renement of existing knowledge because this task requires a three-step process. First, knowledge in some form must be inserted into a neural network. Second, the network must be re ned. Third, knowledge must be extracted from the network. We have previously described a method for the rst step of this proc...

Abstract: In this research, at first, the natural frequencies of a cracked beam are obtained analytically, then, location and depth of a crack in beam is identified by neural network method. The research is applied on a beam with an open crack for three different boundary conditions. For this purpose, at first, the natural frequencies of the cracked beam are obtained analytically, to get the ex...

the hybrid fuzzy differential equations have a wide range of applications in science and engineering. we consider the problem of nding their numerical solutions by using a novel hybrid method based on fuzzy neural network. here neural network is considered as a part of large eld called neural computing or soft computing. the proposed algorithm is illustrated by numerical examples and the resu...

in this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. in proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. in comparison with existing similar neural networks proposed model provides solutions with high accuracy. numerica...

Neural networks, despite their empirically-proven abilities, have been little used for the reenement of existing knowledge because this task requires a three-step process. First, knowledge must be inserted into a neural network. Second, the network must be reened. Third, the reened knowledge must be extracted from the network. We have previously described a method for the rst step of this proce...

‎By p-power (or partial p-power) transformation‎, ‎the Lagrangian function in nonconvex optimization problem becomes locally convex‎. ‎In this paper‎, ‎we present a neural network based on an NCP function for solving the nonconvex optimization problem‎. An important feature of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimizatio...

infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. generally, the infiltrat...

We consider the situation where a nonlinear physical system is identiied from input-output data. In case no speciic physical structural knowledge about the system is available, parameterized grey box models cannot be used. Identiication in black-box-type of model structures is then the only alternative, and general approaches like neural networks, wavelet models, neuro-fuzzy models, etc., have ...