This paper investigated various indoor positioning techniques and presented a comprehensive study about their advantages and disadvantages. Infrared, Ultrasonic and RF technologies are used in different indoor positioning systems. RFID positioning systems based on RSSI technology are the most recent developments. Positioning accuracy was greatly improved by using integrated RFID technologies.

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour based on theoretical models. Instead, current methods to develop robot control code ...

The investigation of robot-environment interaction is the main aim of the RobotMODIC project at the Universities of Essex and Sheffield. The methods developed under this project model and characterise all aspects relevant to the robot’s operation: modelling of sensor perception (“environment identification” or simulation), sensor modelling, and task modelling. In this paper we describe a new pr...

Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...

The problem of identification of a nonlinear dynamic system by using multiple-input and multiple-output flexible neural tree (MIMO-FNT) is presented in this paper. This work is an extension of our previously multiple-input and singleoutput FNT model. FNT is a tree-structured neural networks which allows input variables selection, over-layer connections and different activation functions for dif...

Nonlinear system identification using recurrent neural network with genetic algorithm is presented. A continuous-time model of Hopfield neural network is used in this study. Its convergence properties are first evaluated. Then the model is implemented to identify nonlinear systems. Recurrent network‘s operational factors of the system identification scheme are obtained by genetic algorithm. Mat...

In this thesis three algorithms for the estimation of parameters which occur nonlinearly in dynamic systems are presented. The first algorithm pertains to systems in discrete-time regression form. It is shown that the task of finding an update law for the parameter estimates can be solved numerically by the formulation of a quadratic programming problem. The algorithm does not depend on analyti...

Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained...

This paper investigates error-entropy-minimization in adaptive systems training. We prove the equivalence between minimization of error’s Renyi entropy of order and minimization of a Csiszar distance measure between the densities of desired and system outputs. A nonparametric estimator for Renyi’s entropy is presented, and it is shown that the global minimum of this estimator is the same as the...

When performing nonlinear system identification few tools exist for the a priori nonlinear model structure selection of the nonlinear system. This paper presents a possible approach as a first step towards selecting a nonlineAtr system model structure, based on using the results of Lyapunov exponents, Poincar~ maps and dimension techniques. The approach is illusUated by applying it to the Chua ...