drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

the graphical technique for nonlinear circuits was described that enable us to optimize circuits to obtain maximum output power, maximum efficiency or minimum intermodulation. according to this method a high power amplifier in the ka band was designed. using a nonlinear model of the transistor, optimum slope for load-line was determined so that maximum power at the output was obtainable, then ...

this paper presents an optimal integrated instrumentation sensor network design methodology for complex nonlinear chemical process plants using a combinatorial particle swarm optimiazation (cpso) engine. no comprehensive sensor network design approach has been addressed yet in the literature to simultaneously incorporate cost, precision and reliability requirements for nonlinear plants. the pre...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

in this paper, nonlinear equations of motion for laminated composite rectangular plates based on the first order shear deformation theory were derived. using a perturbation method, the nonlinear equation of motion was solved and analytical relations were obtained for natural and nonlinear frequencies. after proving the validity of the obtained analytical relations, as an alternative and simple ...

in this paper we propose a method for solving some well-known classes of lane-emden type equations which are nonlinear ordinary differential equations on the semi-in nite domain. the proposed approach is based on an unsupervised combined arti cial neural networks (ucann) method. firstly, the trial solutions of the differential equations are written in the form of feed-forward neural networks co...

This paper uses nonlinear regression, Artificial Neural Network (ANN) and Genetic Programming (GP) approaches for predicting an important tangible issue i.e. scours dimensions downstream of inverted siphon structures. Dimensional analysis and nonlinear regression-based equations was proposed for estimation of maximum scour depth, location of the scour hole, location and height of the dune downs...

this paper presents a new control scheme for a class of nonlinear systems. in the proposed method, an adaptive neural network observer with rise feedback controller are applied to realize sensorless control scheme. this observer is tuned online and no exact information of nonlinear term of plant is required. so, this characteristic can compensate mismodeling phenomena. also, a new controller ca...

it is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. hence, at present study, two models including artificial neural networks and nonlinear multivariate regression were used to predict peak discharge in taleghan watershed. maximum daily mean discharge and corresponding daily rainfall, one day antecedent and...