The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed contro...

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...

Aiming at the characteristics which variable air volume air conditioning system is multi-variable, nonlinear and uncertain system, normal fuzzy neural network is hard to meet the requirements which dynamic control of multi-variable. In this paper, we put forward a recursive neural network predictive control strategy based on wavelet neural network model. Through recursive wavelet neural network...

erebellar model arithmetic C computer (CMAC) neural networks can be applied to the problem of biped walking with dynamic balance. The project goal here is to develop biped control strategies based on a hierarchy of simple gait oscillators, PID controllers, and neural network leaming, that do not require detailed kinematic or dynamic models. While results of simulation studies using twodimension...

An adaptive neural network control scheme for thermal power system is described. No off-line training is required for the proposed neural network controller. The online tuning algorithm and neural network architecture are described. The performance of the controller is illustrated via simulation for different changes in process parameters. Performance of neural network controller is compared wi...