Abstract: In this Letter, a class of impulsive switched cellular neural networks with time-varying delays is investigated. At the same time, parametric uncertainties assumed to be norm bounded are considered. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions guaranteeing exponential stability for all admissible parametric uncertainties are derived via constructing appropriate Lyapunov functional. One numerical example is provided to illustrate the validity of the main results obtained in this paper.
Abstract: This paper tests the level of market integration between Malaysia and Singapore stock markets with the world market. Kalman Filter (KF) methodology is used on the International Capital Asset Pricing Model (ICAPM) and the pricing errors estimated within the framework of ICAPM are used as a measure of market integration or segmentation. The advantage of the KF technique is that it allows for time-varying coefficients in estimating ICAPM and hence able to capture the varying degree of market integration. Empirical results show clear evidence of varying degree of market integration for both case of Malaysia and Singapore. Furthermore, the results show that the changes in the level of market integration are found to coincide with certain economic events that have taken placed. The findings certainly provide evidence on the practicability of the KF technique to estimate stock markets integration. In the comparison between Malaysia and Singapore stock market, the result shows that the trends of the market integration indices for Malaysia and Singapore look similar through time but the magnitude is notably different with the Malaysia stock market showing greater degree of market integration. Finally, significant evidence of varying degree of market integration shows the inappropriate use of OLS in estimating the level of market integration.
Abstract: In this paper, we investigate a class of fuzzy Cohen- Grossberg neural networks with time delays and impulsive effects. By virtue of stochastic analysis, Halanay inequality for stochastic differential equations, we find sufficient conditions for the global exponential square-mean synchronization of the FCGNNs under noise perturbation. In particular, the traditional assumption on the differentiability of the time-varying delays is no longer needed. Finally, a numerical example is given to show the effectiveness of the results in this paper.
Abstract: The transient analysis of a queuing system with fixed-size batch Poisson arrivals and a single server with exponential service times is presented. The focus of the paper is on the use of the functions that arise in the analysis of the transient behaviour of the queuing system. These functions are shown to be a generalization of the modified Bessel functions of the first kind, with the batch size B as the generalizing parameter. Results for the case of single-packet arrivals are obtained first. The similarities between the two families of functions are then used to obtain results for the general case of batch arrival queue with a batch size larger than one.
Abstract: In this paper, the problem of estimating the optimal
radio capacity of a single-cell spread spectrum (SS) multiple-inputmultiple-
output (MIMO) system operating in a Rayleigh fading environment
is examined. The optimisation between the radio capacity
and the theoretically achievable average channel capacity (in the
sense of information theory) per user of a MIMO single-cell SS system
operating in a Rayleigh fading environment is presented. Then,
the spectral efficiency is estimated in terms of the achievable average
channel capacity per user, during the operation over a broadcast
time-varying link, and leads to a simple novel-closed form expression
for the optimal radio capacity value based on the maximization
of the achieved spectral efficiency. Numerical results are presented to
illustrate the proposed analysis.
Abstract: This paper presents a new adaptive impedance control
strategy, based on Function Approximation Technique (FAT) to
compensate for unknown non-flat environment shape or time-varying
environment location. The target impedance in the force controllable
direction is modified by incorporating adaptive compensators and the
uncertainties are represented by FAT, allowing the update law to be
derived easily. The force error feedback is utilized in the estimation
and the accurate knowledge of the environment parameters are not
required by the algorithm. It is shown mathematically that the
stability of the controller is guaranteed based on Lyapunov theory.
Simulation results presented to demonstrate the validity of the
proposed controller.
Abstract: In this paper we compare the response of linear and
nonlinear neural network-based prediction schemes in prediction of
received Signal-to-Interference Power Ratio (SIR) in Direct
Sequence Code Division Multiple Access (DS/CDMA) systems. The
nonlinear predictor is Multilayer Perceptron MLP and the linear
predictor is an Adaptive Linear (Adaline) predictor. We solve the
problem of complexity by using the Minimum Mean Squared Error
(MMSE) principle to select the optimal predictors. The optimized
Adaline predictor is compared to optimized MLP by employing
noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an
urban environment. The results show that the Adaline predictor can
estimates SIR with the same error as MLP when the user has the
velocity of 5 km/h and 60 km/h but by increasing the velocity up-to
120 km/h the mean squared error of MLP is two times more than
Adaline predictor. This makes the Adaline predictor (with lower
complexity) more suitable than MLP for closed-loop power control
where efficient and accurate identification of the time-varying
inverse dynamics of the multi path fading channel is required.
Abstract: The paper considers a single-server queue with fixedsize
batch Poisson arrivals and exponential service times, a model
that is useful for a buffer that accepts messages arriving as fixed size
batches of packets and releases them one packet at time. Transient
performance measures for queues have long been recognized as
being complementary to the steady-state analysis. The focus of the
paper is on the use of the functions that arise in the analysis of the
transient behaviour of the queuing system. The paper exploits
practical modelling to obtain a solution to the integral equation
encountered in the analysis. Results obtained indicate that under
heavy load conditions, there is significant disparity in the statistics
between the transient and steady state values.
Abstract: In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.
Abstract: The present work faces the problem of automatic enumeration and recognition of an unknown and time-varying number of environmental sound sources while using a single microphone. The assumption that is made is that the sound recorded is a realization of sound sources belonging to a group of audio classes which is known a-priori. We describe two variations of the same principle which is to calculate the distance between the current unknown audio frame and all possible combinations of the classes that are assumed to span the soundscene. We concentrate on categorizing environmental sound sources, such as birds, insects etc. in the task of monitoring the biodiversity of a specific habitat.
Abstract: This paper focuses on the problem of a common linear copositive Lyapunov function(CLCLF) existence for discrete-time switched positive linear systems(SPLSs) with bounded time-varying delays. In particular, applying system matrices, a special class of matrices are constructed in an appropriate manner. Our results reveal that the existence of a common copositive Lyapunov function can be related to the Schur stability of such matrices. A simple example is provided to illustrate the implication of our results.
Abstract: In this paper, the robust exponential stability problem of discrete-time uncertain stochastic neural networks with timevarying delays is investigated. By introducing a new augmented Lyapunov function, some delay-dependent stable results are obtained in terms of linear matrix inequality (LMI) technique. Compared with some existing results in the literature, the conservatism of the new criteria is reduced notably. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.
Abstract: Most of the commonly used blind equalization algorithms are based on the minimization of a nonconvex and nonlinear cost function and a neural network gives smaller residual error as compared to a linear structure. The efficacy of complex valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies. In this paper we present two neural network models for blind equalization of time-varying channels, for M-ary QAM and PSK signals. The complex valued activation functions, suitable for these signal constellations in time-varying environment, are introduced and the learning algorithms based on the CMA cost function are derived. The improved performance of the proposed models is confirmed through computer simulations.
Abstract: We consider optimal channel equalization for MIMO
(multi-input/multi-output) time-varying channels in the sense of
MMSE (minimum mean-squared-error), where the observation noise
can be non-stationary. We show that all ZF (zero-forcing) receivers
can be parameterized in an affine form which eliminates completely
the ISI (inter-symbol-interference), and optimal channel equalizers
can be designed through minimization of the MSE (mean-squarederror)
between the detected signals and the transmitted signals,
among all ZF receivers. We demonstrate that the optimal channel
equalizer is a modified Kalman filter, and show that under the AWGN
(additive white Gaussian noise) assumption, the proposed optimal
channel equalizer minimizes the BER (bit error rate) among all
possible ZF receivers. Our results are applicable to optimal channel
equalization for DWMT (discrete wavelet multitone), multirate transmultiplexers,
OFDM (orthogonal frequency division multiplexing),
and DS (direct sequence) CDMA (code division multiple access)
wireless data communication systems. A design algorithm for optimal
channel equalization is developed, and several simulation examples
are worked out to illustrate the proposed design algorithm.
Abstract: This paper is concerned with the delay-distributiondependent
stability criteria for bidirectional associative memory
(BAM) neural networks with time-varying delays. Based on the
Lyapunov-Krasovskii functional and stochastic analysis approach,
a delay-probability-distribution-dependent sufficient condition is derived
to achieve the globally asymptotically mean square stable of
the considered BAM neural networks. The criteria are formulated in
terms of a set of linear matrix inequalities (LMIs), which can be
checked efficiently by use of some standard numerical packages. Finally,
a numerical example and its simulation is given to demonstrate
the usefulness and effectiveness of the proposed results.
Abstract: The problem of estimating time-varying regression is
inevitably concerned with the necessity to choose the appropriate
level of model volatility - ranging from the full stationarity of instant
regression models to their absolute independence of each other. In the
stationary case the number of regression coefficients to be estimated
equals that of regressors, whereas the absence of any smoothness
assumptions augments the dimension of the unknown vector by the
factor of the time-series length. The Akaike Information Criterion
is a commonly adopted means of adjusting a model to the given
data set within a succession of nested parametric model classes,
but its crucial restriction is that the classes are rigidly defined by
the growing integer-valued dimension of the unknown vector. To
make the Kullback information maximization principle underlying the
classical AIC applicable to the problem of time-varying regression
estimation, we extend it onto a wider class of data models in which
the dimension of the parameter is fixed, but the freedom of its values
is softly constrained by a family of continuously nested a priori
probability distributions.
Abstract: In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.
Abstract: In this paper, delay-dependent stability analysis for
neutral type neural networks with uncertain paramters and
time-varying delay is studied. By constructing new
Lyapunov-Krasovskii functional and dividing the delay interval into
multiple segments, a novel sufficient condition is established to
guarantee the globally asymptotically stability of the considered
system. Finally, a numerical example is provided to illustrate the
usefulness of the proposed main results.
Abstract: In nature, electromagnetic fields always appear like
atmosphere static electric field, the earth's static magnetic field and
the wide-rang frequency electromagnetic field caused by lightening.
However, besides natural electromagnetic fields (EMF), today human
beings are mostly exposed to artificial electromagnetic fields due to
technology progress and outspread use of electrical devices. To
evaluate nuisance of EMF, it is necessary to know field intensity for
every frequency which appears and compare it with allowed values.
Low frequency EMF-s around transmission and distribution lines are
time-varying quasi-static electromagnetic fields which have
conservative component of low frequency electrical field caused by
charges and eddy component of low frequency magnetic field caused
by currents. Displacement current or field delay are negligible, so
energy flow in quasi-static EMF involves diffusion, analog like heat
transfer. Electrical and magnetic field can be analyzed separately.
This paper analysis the numerical calculations in ELF-400 software
of EMF in distribution substation in shopping center. Analyzing the
results it is possible to specify locations exposed to the fields and
give useful suggestion to eliminate electromagnetic effect or reduce it
on acceptable level within the non-ionizing radiation norms and
norms of protection from EMF.
Abstract: In this paper, by employing a new Lyapunov functional
and an elementary inequality analysis technique, some sufficient
conditions are derived to ensure the existence and uniqueness of
periodic oscillatory solution for fuzzy bi-directional memory (BAM)
neural networks with time-varying delays, and all other solutions of
the fuzzy BAM neural networks converge the uniqueness periodic
solution. These criteria are presented in terms of system parameters
and have important leading significance in the design and applications
of neural networks. Moreover an example is given to illustrate the
effectiveness and feasible of results obtained.