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Title: A neuro approach to solve fuzzy Riccati differential equations

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.
Authors:
 [1] ;  [2] ; ; ;  [1]
  1. InstitutSainsMatematik, Universiti Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur (Malaysia)
  2. (Malaysia)
Publication Date:
OSTI Identifier:
22492495
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1682; Journal 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)
Country of Publication:
United States
Language:
English
Subject:
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