2 edition of **Identification of the coefficient-functions in the general nonlinear input-output model** found in the catalog.

Identification of the coefficient-functions in the general nonlinear input-output model

Gy MikГі

- 267 Want to read
- 34 Currently reading

Published
**1978**
by Dept. of Mathematics, Karl Marx University of Economics in Budapest
.

Written in English

- Input-output analysis.

**Edition Notes**

Statement | by Gy. Mikó. |

Series | DM [report] - Dept. of Mathematics, Karl Marx University of Economics -- 78-7, DM (Series) -- 78-7. |

The Physical Object | |
---|---|

Pagination | 7 p. ; |

ID Numbers | |

Open Library | OL22384752M |

models from empirical input-output data. Most industrial processes, and almost all found in the chemical industry, are multivariable and nonlinear, as well as constantly responding to disturbances that are unmeasurable and occur at unknown times. Although almost all processes are nonlinear, in . approximate non-linear real function with real variables. These real functions may be models for non-linear systems. To obtain these models the users are starting from an initial neural network and train it with a training method, using training set of input-output data of the original system, obtained from practical measurements made upon it.

While integral control lies at the heart of any adaptation method (e.g. model‐reference control, integral backstepping control, autoregressive identification), the time integration involves a nonlinear function of both unknown parameters and input, output or state variables. Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern.

T-S model. 2 Identification of T-S Model An interesting method of identification is presented in [29]. The idea is based on estimating the nonlinear system parameters minimizing a quadratic performance index. The method is based on the identification of functions . Description; Chapters; Supplementary; This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different approaches such as optimization methods for input/output.

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Length limitations restrict these discussions somewhat, but it is hoped that the range of examples will be great enough to demonstrate how nonlinear model identification is both similar to and different from linear model identification. The general conclusion of this paper is that nonlinear input/output modeling is a vitally important practical Cited by: Identification of nonlinear input/output models using non-gaussian input sequences.

In Proc. American Control Conference, pages –, San Francisco, Google ScholarCited by: The Nonlinear Important Coefficients Input-Holding-Output Model3 National planting industry nonlinear input-output model (He, J.M) divides the planting industry into 13 sectors. Based on agriculture nonlinear input-output flow table in constant price it got the nonlinear function relation between intermediate flow and seeding area.

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CHAPTER 1 Input/Output Representations in the Time Domain 1 Linear Systems 1 Homogeneous Nonlinear Systems 3 Polynomial and Volterra Systems 18 Interconnections of Nonlinear Systems 21 Heuristic and Mathematical Aspects.

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VI - System Identification Using Fuzzy Models - Robert Babuška ©Encyclopedia of Life Support Systems (EOLSS) Rii i:if is then is, 1,2.x AyBi K= (5) Here Ai and Bi are linguistic terms (such as ‘small’, ‘large’, etc.), represented by fuzzy sets, and K is the number of rules in the model.Formulas are stated for optimum nonlinear system identification in both general models consisting of parallel, linear bilinear and trilinear systems, and special models consisting of parallel linear, finite-memory square-law systems and finite-memory cubic by: