Identification and control problems for unknown chaotic dynamical systems are considered. Our aim is to regulate the unknown chaos to a fixed point or a stable periodic orbit. This is realized by following two contributions. First, a dynamic neural network is used as identifier. The weights of the neural networks are adjusted by the sliding mode technique. Second, we derive a local optimal cont...

Nonlinear system behavior is not always well characterized by linearized system models, especially if the system is chaotic. This research studies the use of a neural network algorithm structure to model two nonlinear systems, a quadratic system and a chaotic system. An evolutionary programming approach is employed to train the neural nets so that the training process might better avoid selecti...

A chaotic self-organizing map can be produced by replacing the linear neural units of the conventional selforganizing map with neural units capable of producing chaos. The introduction of chaos into the selforganizing map is shown to improve the ability of the network to cluster input patterns. output converges to a single non-zero value. As g is increased beyond 0.75 the output begins to oscil...

this paper presents a new observer-based control scheme for a class of nonlinear systems. in the proposed method, nonlinear observer and twisting algorithm controller are employed to realize a sensor-less control strategy for complex systems which makes use of non measurable process information instead of installing as many sensors as possible. due to lack of availability of the complex system ...

This paper uses the noisy chaotic neural network (NCNN) that we proposed earlier for image denoising as a constrained optimization problem. The experimental results show that the NCNN is able to offer good quality solutions.

Zbigniew Galias, Josef A. Nossek Abstract In this paper we present the laboratory environment and experimental results of stabilization of periodic orbits in a real implementation of a chaotic three-cell cellular neural network.

In this paper, the effect of neuronal gain in discrete delayed neural network model is investigated. It is observed that such neural networks become highly chaotic due to the presence of high neuronal gain. On the basis of the largest Lyapunov exponent and largest eigenvalue of Jacobian matrix, chaos analysis has been done. Finally, some numerical simulations are presented to justify our results.

the safety of buried pipes under repeated load has been a challenging task in geotechnical engineering. in this paper artificial neural network and regression model for predicting the vertical deformation of high-density polyethylene (hdpe), small diameter flexible pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the heavy vehicle loads, are proposed. t...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

The problem of chaotic time series prediction is studied in various disciplines now including engineering, medical and econometric applications. Chaotic time series are the output of a deterministic system with positive Liapunov exponent. A time series prediction is a suitable application for a neuronal network predictor. The NN approach to time series prediction is non-parametric, in the sense...