A neuro approach to solve fuzzy Riccati differential equations

Journal Article · Thu Oct 22 00:00:00 EDT 2015 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.4932411· OSTI ID:22492495

Kamali, M. Z. M.; Ratnavelu, Kurunathan ^[1]

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1. InstitutSainsMatematik, Universiti Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur (Malaysia)

There are many applications of optimal control theory especially in the area of control systems in engineering. In this paper, fuzzy quadratic Riccati differential equation is estimated using neural networks (NN). Previous works have shown reliable results using Runge-Kutta 4th order (RK4). The solution can be achieved by solving the 1st Order Non-linear Differential Equation (ODE) that is found commonly in Riccati differential equation. Research has shown improved results relatively to the RK4 method. It can be said that NN approach shows promising results with the advantage of continuous estimation and improved accuracy that can be produced over RK4.

• OSTI ID: 22492495
• Journal Information: AIP Conference Proceedings, Vol. 1682, Issue 1; Conference: SKSM22: 22. National symposium on mathematical sciences - Strengthening research and collaboration of mathematical sciences in Malaysia, Selangor (Malaysia), 24-26 Nov 2014; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
CONTROL SYSTEMS
FUZZY LOGIC
NEURAL NETWORKS
NONLINEAR PROBLEMS
ONE-DIMENSIONAL CALCULATIONS
OPTIMAL CONTROL
RICCATI EQUATION
RUNGE-KUTTA METHOD