New approaches to generalized Hamiltonian realization of autonomous nonlinear systems

2003 ◽  
Vol 46 (6) ◽  
pp. 431 ◽  
Author(s):  
Yuzhen WANG
Keyword(s):  
Nonlinear Systems ◽  
New Approaches ◽  
Generalized Hamiltonian Realization

Generalized Hamiltonian realization of time-invariant nonlinear systems

Automatica ◽  
2003 ◽  
Vol 39 (8) ◽  
pp. 1437-1443 ◽  
Author(s):  
Yuzhen Wang ◽  
Chunwen Li ◽  
Daizhan Cheng
Keyword(s):  
Nonlinear Systems ◽  
Generalized Hamiltonian Realization ◽  
Time Invariant

scholarly journals A Novel Technique for Solving Fully Fuzzy Nonlinear Systems Based on Neural Networks

2020 ◽  
Vol 07 (01) ◽  
pp. 93-107 ◽  
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov
Keyword(s):  
Neural Networks ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Complex Systems ◽  
Gradient Descent ◽  
Descent Algorithm ◽  
Novel Technique ◽  
New Approaches ◽  
Gradient Descent Algorithm

Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irreducible in several cases. In this paper, two new approaches based on neural networks are proposed in order to find the estimated solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the estimated solutions, a gradient descent algorithm is proposed in order to train the proposed networks. An example is proposed in order to show the efficiency of the considered approaches.

scholarly journals Which Alternative for Solving Dual Fuzzy Nonlinear Equations Is More Precise?

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1507 ◽  
Author(s):  
Joanna Kołodziejczyk ◽  
Andrzej Piegat ◽  
Wojciech Sałabun
Keyword(s):  
Nonlinear Systems ◽  
Nonlinear Equations ◽  
Newton’S Method ◽  
Newton's Method ◽  
Standard Approach ◽  
Fuzzy Arithmetic ◽  
New Approach ◽  
Numerical Example ◽  
New Approaches

To answer the question stated in the title, we present and compare two approaches: first, a standard approach for solving dual fuzzy nonlinear systems (DFN-systems) based on Newton’s method, which uses 2D FN representation and second, the new approach, based on multidimensional fuzzy arithmetic (MF-arithmetic). We use a numerical example to explain how the proposed MF-arithmetic solves the DFN-system. To analyze results from the standard and the new approaches, we introduce an imprecision measure. We discuss the reasons why imprecision varies between both methods. The imprecision of the standard approach results (roots) is significant, which means that many possible values are excluded.

Beyond autism: Challenging unexamined assumptions about social motivation in typical development

2019 ◽  
Vol 42 ◽  
Author(s):  
Karen Bartsch ◽  
David Estes
Keyword(s):  
Social Interest ◽  
Social Motivation ◽  
Typical Development ◽  
Typically Developing ◽  
New Approaches

Abstract In challenging the assumption of autistic social uninterest, Jaswal & Akhtar have opened the door to scrutinizing similar unexamined assumptions embedded in other literatures, such as those on children's typically developing behaviors regarding others’ minds and morals. Extending skeptical analysis to other areas may reveal new approaches for evaluating competing claims regarding social interest in autistic individuals.

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