In the chaotic quantization approach one replaces the Gaussian white noise of the Parisi-Wu approach of stochastic quantization by a deterministic chaotic process on a very small scale. We consider suitable coupled chaotic noise processes as generated by Tchebyscheff maps, and show that the vacuum energy of these models is minimized for coupling constants that coincide with running standard mod...

Problem statement: This study presented a new and systematic method to robustly synchronize uncertain chaos systems. It was derived based on the observer approach for synchronization, where error dynamics was made asymptotically stable around the zero to accomplish synchronization. Approach: This method viewed the synchronization problem as a control problem in order to make use of the literatu...

We propose a novel signal model by combining the sparse stepped frequency signals with chaotic signals, i.e., the sparse stepped chaotic signal (SSCS) model, as well as the corresponding compression algorithm based on compressed sensing. In SSCS, the chaotic signals are modulated to sparse stepped frequencies to compose a transmitting burst. When receiving, the echo signals are demodulated to t...

RNA secondary structure prediction is a computationally feasible and broadly studied problem. It can be considered as the combinatorial optimization problem. In this paper, we propose an improved transiently chaotic neural network (TCNN) for RNA secondary structure prediction. In the improved model, a variable p(t) called the acceptance probability of chaos is introduced into the original TCNN ...

We demonstrate the basic principles for the direct and flexible implementation of all basic logical operations utilizing low dimensional chaos. Then we generalize the concept to high dimensional chaotic systems, and show the parallelism inherent in such systems. As a case study we implement the proposed parallel computing architecture to obtain parallelized bit-by-bit addition with a two-dimens...

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series,...

Lorenz derived the famous three-variable dynamical system [l] that contains chaotic solutions in some parameter anges from a numerical model on 2-dimensional Rayleigh-B&mud convection. He called this kind of highly simplified system low-or&r model. In the fields of Geophysical Fluid Dynamics (GFD) and Climate Dynamics (CD), many descendants of the low-order model have been developed to study th...

This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps predic...

The aim of this paper is to present a fully bounded 2-D model for chaotic attractors or chaotic seas. The relevance of this result is that there are some specific examples of bounded chaotic attractors or chaotic seas. There is no rigorous proof of this property for a general form of mappings. DOI: 10.4018/jalr.2012040105 3 more pages are available in the full version of this document, which ma...

Onset of chaotic mode transitions can be used in a simple and robust mechanism for adaptation in multimode systems. We present an idealized model of adaptive mode selection using on-o switching of chaotic basin transitions a multistable system. A stochastic description obtained in the limit of large switching intervals gives search times in terms of projections of the chaotic state onto basins ...