in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

the purpose of this study was to estimate the torque from high‑density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate‑to‑high isometric elbow flexion‑extension. the elbow torque was estimated in two following steps: first, surface electromyography (emg) amplitudes were estimated using principal com...

This paper presents a concept of a tool for verification of model parameters applicable to dynamic elements of the electric power system. The tool uses the PSLF design software, which is in general use by the transmission system operator. The innovation consists in using an additional application (in MS Windows environment) which controls the work of the design software. This results in a tande...

Ventilation process model is important for automatically appropriate ventilation for patients residing in the intensive care unit (ICU). Based on other researchers’ work, we try to build a data driven ventilation model under the framework of dynamic Bayesian networks (DBNs). All the variables in our model are suggested by the doctor in ICU. They are all noninvasive measurements obtained directl...

This paper discusses the application of support vector machine in the area of identification of nonlinear dynamical systems. The aim of this paper is to identify suitable model structure for nonlinear dynamic system. In this paper, Adaptive Neuro Fuzzy Inference Systems (ANFIS) and Support Vector Regression (SVR) models are applied for identification of highly nonlinear dynamic process. The res...

Learning from examples is one of the key problems in science and engineering. It deals with function reconstruction from a finite set of direct and noisy samples. Regularization in reproducing kernel Hilbert spaces (RKHSs) is widely used to solve this task and includes powerful estimators such as regularization networks. Recent achievements include the proof of the statistical consistency of th...

High performance robot control algorithms often rely on system physical models. For field robots, the dynamic parameters of these physical models may not be well known. This paper presents a new information based performance metric for the on-line dynamic parameter identification of a multi-body system. The metric is used in an algorithm to optimally regulate the external excitation required by...

The model identification of the nonlinear system has been concerned by the industrial community all along. The relationship of the nonlinear dynamic system is contained in the data accumulated in the scene. To better utilize the data about the industrial objects, in this article, we put forward the nonlinear system predictor driven by the Bayesian-Gaussian neural network (NN) model, use the tra...

System identification techniques are powerful tools that help improve modeling capabilities of real world dynamic systems. These techniques are well established and have been successfully used on countless systems in many areas. However, wind turbines provide a unique challenge for system identification because of the difficulty in measuring its primary input: wind. This thesis first motivates ...

due to the fact that the error surface of adaptive infinite impulse response (iir) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. in this case, global optimization techniques are required in order to avoid the local minima. harmony search (hs), a musical inspired metaheuristic, is a recently ...