Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...

A switched-current integrated circuit, which realizes the chaotic neuron model, is presented. The circuit mainly consists of CMOS inverters that are used as transconductance amplifiers and nonlinear elements. The chip was fabricated using a 1.2 μm HP CMOS process. A single neuron cell occupies only 0.0076 mm2, which represents an area smaller than the one occupied by a standard bonding pad. The...

An alternative approach, between much others, for mathematical representation of dynamics systems with complex or chaotic behaviour, is a radial basis function neural network using k-means for clustering and optimized by pseudo-inverse and particle swarm optimisation. This paper presents the implementation and study to identify a dynamic system, with nonlinear and chaotic behaviour, called Röss...

This report describes a neural network architecture ClusNet designed for the prediction of chaotic time series. It advantages include simplicity, fast and sure convergence, and less need for computing resources. After describing its architecture and learning algorithms, its prediction perfo1:mance on the Logistic and the Mackay-Class chaotic time series is presented. Compared to other current p...

The proposed RAITIIFNNC system is comprised of a interval type II fuzzy neural network identifier and a robust controller. The identifier is utilized for online estimation of the compound uncertainties. The robust controller is used to attenuate the effects of the approximation error so that the perfect tracking and synchronization of chaotic systems are achieved. All the parameter learning alg...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

In this paper a combined controller is proposed for nonlinear dynamical systems. The controller is constructed by a fuzzy wavelet network and nonlinear model predictive control. Chaotic optimization, which is fast and robust, is applied to generate optimized controlled input in nonlinear model predictive control. The ability of the fuzzy wavelet neural network and the proposed controller is sho...

We propose an efficient hybrid neural network for chaotic time series prediction. The hybrid neural network is constructed by a traditional feed-forward network, which is learned by using the backpropagation and a local model, which is implemented as a time delay embedding. The feed-forward network performs as the global approximation and the local model works as the local approximation. Experi...

The model of neural networks on the small-world topology, with metric (local and random connectivity) is investigated. The synaptic weights are random, driving the network towards a chaotic state for the neural activity. An ordered macroscopic neuron state is induced by a bias in the network connections. When the connections are mainly local, the network emulates a block-like structure. It is f...