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In this paper, the existence of periodic and partly periodic oscillation for a recurrent neural network with time-varying input and time delays between neural interconnections is investigated. Some theorems to determine the conditions for periodic oscillations are demonstrated. Simple and practical criteria for selecting the parameters in this network are derived. Typical simulation examples are also...
Objective sentences lack sentiments and, hence, can reduce the accuracy of a sentiment classifier. Traditional methods prior to 2001 used hand-crafted templates to identify subjectivity and did not generalize well for resource-deficient languages such as Spanish. Later works published between 2002 and 2009 proposed the use of deep neural networks to automatically learn a dictionary of features (in...
This paper presents a Genetic Algorithm (GA) based evolution framework in which Spiking Neural Network (SNN) of single or a colony of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed of randomly connected Izhikevich spiking reservoir neural networks. Inspired by biological neurons, the neuronal connections are considered...
This paper introduces a novel design of phase locked loop (PLL) based oscillatory neural networks (ONNs) to mitigate the frequency clustering phenomenon caused by transmission delays in real systems. Theoretical analysis of the ONN reveals that transmission delays can produce frequency clustering that leads to synchronization and convergence failure. This paper describes the redesign of ONN dynamics...
By further extending SpikeProp, we propose a backpropagation learning algorithm, which adjusts all the parameters, synaptic weights, synaptic delays, synaptic time constants, and neurons' thresholds, for spiking neural networks with multiple layers and multiple spiking neurons.
In this paper, we consider a novel approach to information representation in spiking neural networks. In a certain sense it is a combination of two well-known coding schemes - rate coding and temporal coding. Namely, it is based on asynchronous activity of ensembles of polychronous neuronal groups - groups of neurons firing in determined order with strictly fixed relative temporal delays. We demonstrate...
By the combination of the adaptive backstepping design with the dynamic surface control technique, an novel adaptive neural control approach is investigated for a class of pure-feedback stochastic nonlinear systems with multiple unknown time-varying delays. To overcome the design difficulty arising from the non-affine structure of pure-feedback stochastic systems, the mean value theorem is exploited...
We present a networked control system for nonlinear plants with unknown dynamics using a wavelet neural network (WNN) with feedforward component and generalized predictive controller (GPC). The WNN with feedforward term is used to adaptively model the nonlinear plant using its input and output history. The WNN uses the traditional back propagation algorithm and the feedforward term uses the recursive...
This work is concerned with the existence and uniqueness of pseudo almost-periodic solution for a class of impulsive recurrent neural network networks with mixed delays. Some criteria are established to prove the asymptotic stability of the equilibrium point. Tow illustrative an example with numerical simulations are given to show the validity of the main results.
Delay learning in SpikeProp is useful because it eliminates the need of redundant synaptic connections in a Spiking Neural Network (SNN). The delay learning enhancement to SpikeProp, however, also inherits the complications present in basic SpikeProp with weight update that obstruct the learning process. To tackle these issues, we perform delay convergence analysis to investigate the conditions required...
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