Sharif Digital Repository

In this paper, a novel approach for tuning the parameters of fuzzy wavelet systems which are used for modeling of nonlinear and complex systems is proposed. In fuzzy inference system, each fuzzy rule is analogous to a wavelet basis function multiplied by a coefficient. Using clustering techniques, the center of these basis functions are located in the detected center of clusters. In this way, not only the approximation accuracy is increased, but also the number of unknown parameters is decreased. The feasibility of the proposed method is shown by modeling two highly nonlinear functions. The comparison of the results using the proposed approach, with the previous schemes, shows the... 

Recently online prediction of plate deformations in modern systems have been considered by many researchers, common standard methods are highly time consuming and powerful processors are needed for online computation of deformations. Artificial neural networks have capability to develop complex, nonlinear functional relationships between input and output patterns based on limited data. A good trained network could predict output data very fast with acceptable accuracy. This paper describes the application of an artificial neural network to identify deformation pattern of a four-side clamped plate under external loads. In this paper the distributed loads are approximated by a set of... 

Statistical process control methods for monitoring processes with multivariate measurements in both the product quality variable space and process variable space are considered in this paper. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, we assume that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of... 

This paper presents a particle swarm optimization (PSO) based approach to solve the multi-stage transmission expansion planning problem in a competitive pool-based electricity market. It is a large-scale non-linear combinatorial problem. We have considered some aspects in our modeling including a multi-year time horizon, a number of scenarios based on the future demands of system, investment and operating costs, the N - 1 reliability criterion, and the continuous non-linear functions of market-driven generator offers and demand bids. Also the optimal expansion plan to maximize the cumulative social welfare among the multi-year horizon is searched. Our proposed PSO based approach, namely... 

We study the problem of transmission of binary input non-linear functions over a network of mobiles based on CDMA. Motivation for this study comes from the application of using cheap measurement devices installed on personal cellphones to monitor environmental parameters such as air pollution, temperature and noise level. Our model resembles the MAC model of Nazer and Gastpar except that the encoders are restricted to be CDMA encoders. Unlike the work of Nazer and Gastpar whose main attention is transmission of linear functions, we deal with non-linear functions with binary inputs. A main contribution of this paper is a lower bound on the computational capacity for this problem. While in the... 

This paper introduces an incremental local learning algorithm inspired by learning in neurobiological systems. This algorithm has no training phase and learns the world during operation, in a lifetime manner. It is a semi-supervised algorithm which combines soft competitive learning in input space and linear regression with recursive update in output space. This method is also robust to negative interference and compromises bias-variance dilemma. These qualities make the learning method a good nonlinear function approximator having possible applications in neuro-robotic systems. Some simulations illustrate the effectiveness of the proposed algorithm in function approximation, time-series... 

For various types of materials, milling process is extensively used to generate complex shapes with high quality. During the process and to achieve high removal rate, precision and better surface finish, chatter suppression is of great importance. An extended model of the milling process is presented in which the cutting forces are described as a third-order nonlinear function of chip thickness. Uncertainties associated with the process and tool parameters are also included to achieve a more realistic model. To suppress regenerative chatter, an H∞ robust control is designed based on µ-synthesis with DK-iteration algorithm. The controller guarantees the robust stability and performance of... 

This work deals with the robust D-stability test of linear time-invariant ( LTI ) general fractional order control systems in a closed loop where the system and - or the controller may be of fractional order. The concept of general implies that the characteristic equation of the LTI closed loop control system may be of both commensurate and non-commensurate orders, both the coefficients and the orders of the characteristic equation may be nonlinear functions of uncertain parameters, and the coefficients may be complex numbers. Some new specific areas for the roots of the characteristic equation are found so that they reduce the computational burden of testing the robust D-stability. Based on... 

Fast finite time adaptive sliding mode control of a quadrotor in the presence of uncertainties and unbounded external disturbances is dealt in this paper. To this end, a fractional order sliding surface is first defined and then, an adaptive sliding mode controller is designed to guarantee finite time stability with fast convergence of quadrotor states to the desired trajectory. In this controller, it is assumed that the upper bound of the model uncertainties and external disturbances is a nonlinear function with unknown coefficients. These coefficients are estimated via stable adaptive laws. Finite time stability of the closed-loop system is analyzed using Lyapunov theorem. Simulation... 

This paper presents an optimization model for the calculation of the minimum number of phasor measurement units (PMUs) in electrical power networks. The problem constraint is a predefined probability of observability associated with each bus. The mixed-integer programming is used for the proposed optimization and an efficient linearization technique is proposed to convert the nonlinear function representing the probability of observability into a set of linear expressions. The PMU placement is staged in a multi-year planning horizon due to financial and physical constraints. The average probability of observability is maximized at the intermediate planning stages, subject to a limited number... 

In this paper size-dependent static and dynamic behavior of nonlinear Euler-Bernoulli beams made of functionally graded materials (FGMs) is investigated on the basis of the strain gradient theory. The volume fraction of the material constituents is assumed to be varying through the thickness of the beam based on a power law. As a consequence, the material properties of the microbeam (including length scales) are varying in the direction of the beam thickness. To develop the model, the usual simplifying assumption which considers the length scale parameter to be constant through the thickness is avoided and equivalent length scale parameters are introduced for functionally graded microbeams... 

We propose a robust deviation framework to deal with uncertain component reliabilities in the constrained redundancy optimization problem (CROP) in series-parallel reliability systems. The proposed model is based on a linearized binary version of standard nonlinear integer programming formulations of this problem. We extend the linearized model to address uncertainty by assuming that the component reliabilities belong to an interval uncertainty set, where only upper and lower bounds are known for each component reliability, and develop a Min-Max regret model to handle data uncertainty. A key challenge is that, because the deterministic model involves nonlinear functions of the uncertain... 

In this paper, a recurrent neural network model is developed in order to forecast the energy consumption as a complex nonlinear function of gross domestic product (GDP) and population in Iran. This intelligent model is trained by total energy consumption data as output and the population and GDP as inputs during 1976-2001, while 5 annual data points of the following years (2002-2006) are used to validate the model. It can describe time dependencies efficiently and the convergence rate is much faster. This model forecasts the trend of energy consumption annually. Simulation results show that this model can predict energy consumption in Iran with acceptable accuracy. It is expected that this... 

This paper presents an optimization model for the calculation of the minimum number of phasor measurement units (PMUs) in electrical power networks. The problem constraint is a predefined probability of observability associated with each bus. The mixed-integer programming is used for the proposed optimization and an efficient linearization technique is proposed to convert the nonlinear function representing the probability of observability into a set of linear expressions. The PMU placement is staged in a multi-year planning horizon due to financial and physical constraints. The average probability of observability is maximized at the intermediate planning stages, subject to a limited number... 

This paper presents a robust model predictive control algorithm with a time-varying terminal constraint set for systems with model uncertainty and input constraints. In this algorithm, the nonlinear system is approximated by a linear model where the approximation error is considered as an unstructured uncertainty that can be represented by a Lipschitz nonlinear function. A continuum of terminal constraint sets is constructed off-line, and robust stability is achieved on-line by using a variable control horizon. This approach significantly reduces the computational complexity. The proposed robust model predictive controller with a terminal constraint set is used in tracking set-points for... 

The activity coefficient at infinite dilution is a representative of the limiting non-ideality of a solute in a mixture. Various methods for the prediction of infinite dilution activity coefficients (IDACs) have been developed. Artificial neural networks are powerful mapping tools for nonlinear function approximations. Accordingly, an artificial neural network model is proposed for the prediction of the IDACs of binary systems where the properties of the individual components are used as inputs to the network. The input parameters of the neural network are the mixture temperature, critical temperature, critical pressure, critical volume, molecular weight, dipole moment and the acentric... 

The railroad blocking problem emerges as an important issue at the tactical level of planning in freight rail transportation. This problem consists of determining the optimal paths for freight cars in a rail network. Often, demand and supply resource indicators are assumed certain; hence, the solution obtained from a certain model might not be optimal or even feasible in practice due to the stochastic nature of these parameters. To address this issue, this paper develops a robust model for this problem with uncertain demand and travel time as supply resource indicators. Since the model combines integer variables and nonlinear functions, a branch-And-cut algorithm is used to solve the... 

This paper presents a decentralized adaptive control design for a class of large-scale nonlinear systems with unknown subsystems. When the subsystems are modeled by affine equations, a direct adaptive controller is devised based on the Lyapunov theory, so that the stability of the closed-loop system is guaranteed by introducing a suitably driven adaptive rule. A neuro-based structure is proposed when the subsystems are nonaffine, and the stability analysis is also performed based on the Lyapunov theory. Moreover, the unknown interactions among the subsystems are considered as having a nonlinear function against the simple form considered for the affine case. The proposed controllers are... 

We propose a new nonlinear Rao-Nam like symmetric key encryption scheme. In our design, we employ a specific type of coded modulation schemes namely quasi-cyclic low-density parity-check (QC-LDPC) lattice-codes which have low-complexity encoding and decoding algorithms. Due to the application of coded modulation schemes in our design, the proposed scheme performs encryption, encoding and modulation simultaneously. Therefore, we regard the proposed scheme as a joint cryptosystem. The proposed joint cryptosystem withstands all variants of chosen plaintext attacks applied on Rao-Nam like cryptosystems due to its nonlinearity. Moreover, some conditions implying the uniformity of the ciphertexts... 

Statistical process control methods for monitoring processes with univariate or multivariate measurements are used widely when the quality variables fit to known probability distributions. Some processes, however, are better characterized by a profile or a function of quality variables. For each profile, it is assumed that a collection of data on the response variable along with the values of the corresponding quality variables is measured. While the linear function is the simplest, it occurs frequently that many of the nonlinear functions may be transferred to linear functions easily. This paper proposes a control chart based on the generalized linear test (GLT) to monitor coefficients of...