A new approach for neural control of nonlinear discrete dynamic systems

The periodic nature of the positive solutions of a nonlinear fuzzy max-difference equation

“…We can prove that the solution (y n , z n ) of (10) is eventually periodic with period 2k, using the same argument as in Lemma 3.1 and Theorem 3.1 of [28]. We mention that, a sketch of this proof is given in Proposition 3 that follows, where we prove the corresponding result for the fuzzy difference equation (4). Now, suppose that relations (12) hold.…”

“…We say that x n is a positive solution of (4) if x n is a sequence of positive fuzzy numbers which satisfies (4).…”

“…The first is that the mathematical modelling of a real world phenomenon, very often, leads to an initial value problem of ordinary difference equations (for partial review of the theory of difference equations and their 0020 applications see [1,3,4,7,8,13]). The second is that the initial value may be imprecise and uncertain and so the model may contain uncertain parameters.…”

Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms

“…In [2] a new scheme for on-line states and parameters estimation of a large class of nonlinear systems using radial basis function neural network has been designed. A new approach to control nonlinear discrete dynamic systems, which relies on the identification of a discrete model of the system by a feedforward neural network with one hidden layer, is presented in [3]. Nonlinear system identification via discrete-time recurrent single layer and multilayer neural networks are studied in [4].…”

The potentional application of artificial intelligence in motor vehicles braking system performance

“…Stabilnost ovakvih varijabilnih modela može biti verifikovana pomoću teorije Ljapunova. Novi pristup za upravljanje nelinearnim diskretnim dinamičkim sistemima, koji se oslanja na identifikaciju diskretnog modela sistema pomoću neuronske mreže, predložen je u [79]. U [80] je analizirano na koji način arhitekture veštačkih neuronskih mreža utiču na uspešnost predviđanja, tj.…”