No abstract

Not only does the modeling of dynamical systems, for instance the biological systems, play an important role in the accurate perception and analysis of these systems, but it also makes the prediction and control of their behavior straightforward. The results of multiple researches in the field of the modeling of biological systems have indicated that the chaotic behavior is a prevalent feature of most complex interactive biological systems. Our results demonstrate that the artificial neural network provides us an effective means to model the underlying dynamics of these systems. In this paper, at first, we represent the results of the use of a multilayer feed-forward neural network to model some famous chaotic systems. The specified neural network is trained with the return maps extracted from the time series. We proceed with the paper by evaluating the accuracy and robustness of our model. The ability of the select neural network to model the dynamics of chosen chaotic systems is verified, even in the presence of noise. Afterwards, we model the brain response to the flicker light. It is known that the brain response to some stimuli such as the flicker light recorded as electroretinogram is an exemplar of chaotic behavior. The need remains, however, for realistic modeling of this behavior of the brain. In this paper, we represent the results of the modeling of this chaotic response by utilizing the proposed neural network. The capability of the neural network to model this specific brain response is confirmed.

Initial estimation is a considerable issue in channel estimation techniques, since all of the following processes depends on it, which in this paper its improvement is discussed. Least Square (LS) method is a common simple way to estimate a channel initially but its efficiency is not as significant as more complex approaches. It is possible to enhance channel estimation performance by using some methods such as principal component analysis (PCA), which is not prevalent in channel estimation, and its adaptation to channel information can be challenging. PCA method improves initial estimation performance by projecting data onto direction of eigenvectors by means of using simple algebra. In this paper, channel estimation is examined in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, with significant advantages such as an acceptable performance in frequency selective fading channel. Moreover the proposed channel estimation method manipulates the benefits of MIMO channel by using the information, gained by all channels to estimate the information of each receiver

The recent meteoric significant developments in the biological and medical sciences have been the culmination of substantial efforts devoted to precisely modeling the behavior of biological systems and their responses to various stimuli. The complicated interactions within varied components of biological systems as well as with their environments make them extremely complex nonlinear systems. The results of several contemporary relevant investigations have manifested their chaotic behavioral patterns. With the aim of modeling this specific behavior of biosystems, we employ a particular multilayer feed-forward neural network. The distinctive feature of our modeling method, which makes it dominant within the modeling techniques, is training the select neural network with the chaotic map extracted from the under-study time series. Our results, which are briefly represented in this paper, confirm that the specified neural network does possess the potentiality to model the chaotic dynamics of biological systems, even in the presence of noise. In pursuance of evaluating our model, we assess and model the chaotic response of the brain to the flicker light through some recorded electroretinogram data.

The precipitous advancements in the field of modeling of dynamical systems, which are the result of numerous relevant investigations, are the evidence of its fundamental importance. Not only does the modeling of the behavior of dynamical systems such as biological systems play an important role in the accurate perception and analysis of these systems, but it also becomes feasible to perfectly predict and control their behaviors. The results of the majority of these researches have indicated that chaotic behavior is a prevalent feature of complex interactive systems. Our achieved results indicate that artificial neural networks provide us the most efficacious means to model the underlying dynamics of these systems. In this paper, we represent the results of utilizing a specific neural network to model some famous chaotic systems such as Lorenz. The main aspect of our technique is training the neural network with a chaotic map. With this aim, at first, bifurcation diagram of the points obtained by applying Poincare section on the time series is plotted. The specified neural network is then trained with the extracted map. We conclude the paper by evaluating the accuracy and robustness of our model. The capability of the selected neural network to model the complex behavior of dynamical systems is indeed verified, even at the presence of noise.