In this paper, we propose a novel Takagi-SugenoKang type interval-valued neural fuzzy system with asymmetric fuzzy membership functions (called TIVNFS-A). In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. Based on the Lyapunov stability theorem, the TIVNFS-A system is trained by the back-propag...

Bilinear systems offer a promising approach for nonlinear control because a broad class of nonlinear problems can be reformulated in bilinear form. In this paper system identification is shown to be a technique to obtain such a bilinear approximation of a nonlinear system. Recent discrete-time bilinear model identification methods rely on Input-Output-to-State Representations. These IOSRs are e...

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...

Volterra series based model, which is comprised of uniand multi-convolutions in terms of various inputs, could provide an accurate description of nonlinearities while preserving memory effects missed in static transformations. The basic premise of the Volterra theory of nonlinear systems is that any nonlinear system can be modeled as an infinite sum of multidimensional convolution integrals of ...

this paper proposes a three-step method for solving nonlinear volterra integralequations system. the proposed method convents the system to a (3 × 3)nonlinear block system and then by solving this nonlinear system we ndapproximate solution of nonlinear volterra integral equations system. to showthe advantages of our method some numerical examples are presented.

Conventional Volterra series model is hardly applied to engineering practice due to its parametric complexity and estimation difficulty. To solve this problem, nonlinear system identification using reduced complexity Volterra models is proposed. Since the nonlinear components often play a secondary role compared to the dominant, linear component of the system, they spend the most of identificat...

The Bussgang coefficient is calculated for a memoryless nonlinear system and the concept extended to a dynamic system modeled by a Volterra Series and for Gaussian Inputs. The theory obtained is then applied to a simple system and the underlying linear system obtained for different system configurations.

In this paper, we present gradient expressions for a closed-loop parametric identification scheme. The method is based on the minimization of a standard identification criterion and a parametrization that is tailored to the closed-loop configuration. It is shown that for both linear and nonlinear plants and controllers, the gradient signals can be computed exactly. 1999 Elsevier Science Ltd. Al...