in this paper modeling and forecasting of revenue of taxes in fifth development plan is investigated based on a special structure of nonlinear neural networks. the time series of taxes which are studied in this research are related to total tax, direct tax, indirect tax, companies’ tax, income tax, wealth tax, and import tax. based on the correlation dimension estimation technique, the structur...

In this paper, a robust nonlinear controller is designed in the Input/Output (I/O) linearization framework, for non-square multivariable nonlinear systems that have more inputs than outputs and are subject to parametric uncertainty. A nonlinear state feedback is synthesized that approximately linearizes the system in an I/O sense by solving a convex optimization problem online. A robust control...

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, ...

the deformation modulus of a rock mass is an important input parameter in any analysis of rock mass behavior for geotechnical projects such as dams. the determination of this parameter by in situ tests are time consuming and expensive. therefore, different researchers have proposed empirical relationships for estimating the value of rock mass deformation modulus. this work has been done by usin...

In this paper, nonlinear receding horizon control (NMPC) is implemented on a laboratory quadruple-tank system, which is a nonlinear multi-variable process with state constraints as well as input constraints. A fast numerical algorithm called C/GMRES is employed to implement nonlinear receding horizon control of quadruple-tank system, in which the continuation method is combined with a fast algo...

This paper presents a method of MRAC(model reference adaptive control) for multi-input multi-output(MIMO) nonlinear systems using NNs(neural networks). The control input is given by the sum of the output of a model reference adaptive controller and the output of the NN(neural network). The NN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the us...

This paper presents an approach of achieving high performance and robustness for matched uncertain multi-input multi-output linear systems with external disturbances and multiple state-delays, which are often encountered in practice and are frequently the sources of instability. This scheme is based on composite nonlinear feedback and integral sliding mode control methods. The selection of nonl...

– The paper presents an effective procedure for control design of multi input – multi output nonlinear processes. The procedure is based on an approximation of a nonlinear model of the process by a continuoustime external linear model in the form of the left polynomial matrix fraction. The parameters of the continuoustime external linear model are recursively estimated either by a direct method...