With the rapid development of wireless communication technology, the wireless spectrum resources are dwindling. Cognitive radio (CR) is the key technology to solve this problem. In view of the echo state network advantages compared to traditional recursive neural network, we construct the new neural network, echo state network based on leaky integrate and fire neurons (LIF_ESN), and prove that ...

0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.09.049 * Corresponding author. Tel.: +86 10 62777703. E-mail addresses: [email protected] com (Z. Yang), [email protected] (Y. Song). Neural network has been popular in time series prediction in financial areas because of their advantages in handling nonlinear systems. This paper presents a study of using a no...

The prediction of Bit Error Rate (BER) in OFDMA Channel (IEEE 802.16e Mobile WirlessMAN) network is investigated here. The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the best system to predict and analyze the behaviors of such nonlinear and temporal sy...

Echo state networks (ESN) are a novel approach to recurrent neural network training. An ESN consists of a large, fixed, recurrent "reservoir" network, from which the desired output is obtained by training suitable output connection weights. Determination of optimal output weights becomes a linear, uniquely solvable task of MSE minimization. This article reviews the basic ideas and describes an ...

Time-series data is an appealing study topic in mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods become mainstream. Echo State Networks (ESN) Convolutional Neural (CNN) are commonly utilized as TSC research. However, ESN CNN can only extract local...

Abstract. The properties of the auroral electrojets are examined on basis a trained machine-learning model. relationships between solar-wind parameters and AU AL indices modeled with an echo state network (ESN), kind recurrent neural network. We can consider this ESN model to represent nonlinear effects inputs electrojets. To identify electrojets, we obtain various synthetic data by using artif...

Modeling dynamical systems via neural networks has become a well-established field of research in Computer Science. However, for more complex dynamical systems the current algorithms are often impractical or not accurate enough. Recurrent Neural Networks (RNNs), when coupled with the Echo-State Network (ESN) approach, offer an efficient way of solving such kind of problems – by concentrating on...

A lot of attention is now being focused on connectionist models known under the name “reservoir computing”. The most prominent example of these approaches is a recurrent neural network architecture called an echo state network (ESN). ESNs were successfully applied in more real-valued time series modeling tasks and performed exceptionally well. Also using ESNs for processing symbolic sequences s...

Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the “reservoir”) and an adaptable readout from the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly...