Abstract: Let T,U and V be rings with identity and M be a unitary (T,U)-bimodule, N be a unitary (U, V )- bimodule, D be a unitary (T, V )-bimodule . We characterize homomorphisms and isomorphisms of the generalized matrix ring Γ = T M D 0 U N 0 0 V .
Abstract: Most of the nonlinear equation solvers do not converge always or they use the derivatives of the function to approximate the
root of such equations. Here, we give a derivative-free algorithm that guarantees the convergence. The proposed two-step method, which
is to some extent like the secant method, is accompanied with some
numerical examples. The illustrative instances manifest that the rate of convergence in proposed algorithm is more than the quadratically
iterative schemes.
Abstract: Advances in computing applications in recent years
have prompted the demand for more flexible scheduling models for
QoS demand. Moreover, in practical applications, partly violated
temporal constraints can be tolerated if the violation meets certain
distribution. So we need extend the traditional Liu and Lanland model
to adapt to these circumstances. There are two extensions, which are
the (m, k)-firm model and Window-Constrained model. This paper
researches on weakly hard real-time constraints and their combination
to support QoS. The fact that a practical application can tolerate some
violations of temporal constraint under certain distribution is
employed to support adaptive QoS on the open real-time system. The
experiment results show these approaches are effective compared to
traditional scheduling algorithms.
Abstract: In this paper, we suggest new product-type estimators for the population mean of the variable of interest exploiting the first or the third quartile of the auxiliary variable. We obtain mean square error equations and the bias for the estimators. We study the properties of these estimators using simple random sampling (SRS) and ranked set sampling (RSS) methods. It is found that, SRS and RSS produce approximately unbiased estimators of the population mean. However, the RSS estimators are more efficient than those obtained using SRS based on the same number of measured units for all values of the correlation coefficient.
Abstract: In this paper an efficient incomplete factorization preconditioner is proposed for the Least Mean Squares (LMS) adaptive filter. The proposed preconditioner is approximated from a priori knowledge of the factors of input correlation matrix with an incomplete strategy, motivated by the sparsity patter of the upper triangular factor in the QRD-RLS algorithm. The convergence properties of IPLMS algorithm are comparable with those of transform domain LMS(TDLMS) algorithm. Simulation results show efficiency and robustness of the proposed algorithm with reduced computational complexity.
Abstract: In this paper, by applying Mawhin-s continuation theorem of coincidence degree theory, we study the existence of almost periodic solutions for neural multi-delay logarithmic population model and obtain one sufficient condition for the existence of positive almost periodic solution for the above equation. An example is employed to illustrate our result.
Abstract: The objective of global optimization is to find the
globally best solution of a model. Nonlinear models are ubiquitous
in many applications and their solution often requires a global
search approach; i.e. for a function f from a set A ⊂ Rn to
the real numbers, an element x0 ∈ A is sought-after, such that
∀ x ∈ A : f(x0) ≤ f(x). Depending on the field of application,
the question whether a found solution x0 is not only a local minimum
but a global one is very important.
This article presents a probabilistic approach to determine the
probability of a solution being a global minimum. The approach is
independent of the used global search method and only requires a
limited, convex parameter domain A as well as a Lipschitz continuous
function f whose Lipschitz constant is not needed to be known.
Abstract: Biological evolution has generated a rich variety of
successful solutions; from nature, optimized strategies can be
inspired. One interesting example is the ant colonies, which are able
to exhibit a collective intelligence, still that their dynamic is simple.
The emergence of different patterns depends on the pheromone trail,
leaved by the foragers. It serves as positive feedback mechanism for
sharing information.
In this paper, we use the dynamic of TASEP as a model of
interaction at a low level of the collective environment in the ant-s
traffic flow. This work consists of modifying the movement rules of
particles “ants" belonging to the TASEP model, so that it adopts with
the natural movement of ants. Therefore, as to respect the constraints
of having no more than one particle per a given site, and in order to
avoid collision within a bidirectional circulation, we suggested two
strategies: decease strategy and waiting strategy. As a third work
stage, this is devoted to the study of these two proposed strategies-
stability. As a final work stage, we applied the first strategy to the
whole environment, in order to get to the emergence of traffic flow,
which is a way of learning.
Abstract: In the paper, based on stochastic analysis theory and Lyapunov functional method, we discuss the mean square stability of impulsive stochastic delay differential equations with markovian switching and poisson jumps, and the sufficient conditions of mean square stability have been obtained. One example illustrates the main results. Furthermore, some well-known results are improved and generalized in the remarks.
Abstract: We employ the idea of Hirota-s bilinear method, to obtain some new exact soliton solutions for high nonlinear form of (2+1)-dimensional potential Kadomtsev-Petviashvili equation. Multiple singular soliton solutions were obtained by this method. Moreover, multiple singular soliton solutions were also derived.
Abstract: This paper proposes a novel feature extraction method,
based on Discrete Wavelet Transform (DWT) and K-L Seperability
(KLS), for the classification of Functional Data (FD). This method
combines the decorrelation and reduction property of DWT and the
additive independence property of KLS, which is helpful to extraction
classification features of FD. It is an advanced approach of the
popular wavelet based shrinkage method for functional data reduction
and classification. A theory analysis is given in the paper to prove the
consistent convergence property, and a simulation study is also done
to compare the proposed method with the former shrinkage ones. The
experiment results show that this method has advantages in improving
classification efficiency, precision and robustness.
Abstract: Water pollution assessment problems arise frequently
in environmental science. In this research, a finite difference method
for solving the one-dimensional steady convection-diffusion equation
with variable coefficients is proposed; it is then used to optimize
water treatment costs.
Abstract: A method for solving linear and non-linear Goursat
problem is given by using the two-dimensional differential transform
method. The approximate solution of this problem is calculated in
the form of a series with easily computable terms and also the exact
solutions can be achieved by the known forms of the series solutions.
The method can easily be applied to many linear and non-linear
problems and is capable of reducing the size of computational work.
Several examples are given to demonstrate the reliability and the
performance of the presented method.
Abstract: This study deals with the phenomena of reflection and transmission (refraction) of qSV-waves, for an incident of quasi transverse vertically waves, at a plane interface of two semi-infinite piezoelectric elastic media under the influence of the initial stresses. The relations governing the reflection and transmission coefficients of these reflected waves for various suitable boundary conditions are derived. We have shown analytically that reflection and transmission coefficients of (qP) and (qSV) waves depend upon the angle of incidence, the parameters of electric potential, the material constants of the medium as will as the initial stresses presented in the media. The numerical calculations of the reflection and transmission amplitude ratios for different values of initial stresses have been carried out by computer for different materials as examples and the results are given in the form of graphs. Finally, some of particular cases are considered.
Abstract: In this work, we consider the number of integer solutions
of Diophantine equation D : y2 - 2yx - 3 = 0 over Z and
also over finite fields Fp for primes p ≥ 5. Later we determine the
number of rational points on curves Ep : y2 = Pp(x) = yp
1 + yp
2
over Fp, where y1 and y2 are the roots of D. Also we give a formula
for the sum of x- and y-coordinates of all rational points (x, y) on
Ep over Fp.
Abstract: Statistical selection procedures are used to select the
best simulated system from a finite set of alternatives. In this paper,
we present a procedure that can be used to select the best system
when the number of alternatives is large. The proposed procedure
consists a combination between Ranking and Selection, and Ordinal
Optimization procedures. In order to improve the performance of Ordinal
Optimization, Optimal Computing Budget Allocation technique
is used to determine the best simulation lengths for all simulation
systems and to reduce the total computation time. We also argue
the effect of increment in simulation samples for the combined
procedure. The results of numerical illustration show clearly the effect
of increment in simulation samples on the proposed combination of
selection procedure.
Abstract: The quality of a machined surface is becoming more and more important to justify the increasing demands of sophisticated component performance, longevity, and reliability. Usually, any machining operation leaves its own characteristic evidence on the machined surface in the form of finely spaced micro irregularities (surface roughness) left by the associated indeterministic characteristics of the different elements of the system: tool-machineworkpart- cutting parameters. However, one of the most influential sources in machining affecting surface roughness is the instantaneous state of tool edge. The main objective of the current work is to relate the in-process immeasurable cutting edge deformation and surface roughness to a more reliable easy-to-measure force signals using a robust non-linear time-dependent modeling regression techniques. Time-dependent modeling is beneficial when modern machining systems, such as adaptive control techniques are considered, where the state of the machined surface and the health of the cutting edge are monitored, assessed and controlled online using realtime information provided by the variability encountered in the measured force signals. Correlation between wear propagation and roughness variation is developed throughout the different edge lifetimes. The surface roughness is further evaluated in the light of the variation in both the static and the dynamic force signals. Consistent correlation is found between surface roughness variation and tool wear progress within its initial and constant regions. At the first few seconds of cutting, expected and well known trend of the effect of the cutting parameters is observed. Surface roughness is positively influenced by the level of the feed rate and negatively by the cutting speed. As cutting continues, roughness is affected, to different extents, by the rather localized wear modes either on the tool nose or on its flank areas. Moreover, it seems that roughness varies as wear attitude transfers from one mode to another and, in general, it is shown that it is improved as wear increases but with possible corresponding workpart dimensional inaccuracy. The dynamic force signals are found reasonably sensitive to simulate either the progressive or the random modes of tool edge deformation. While the frictional force components, feeding and radial, are found informative regarding progressive wear modes, the vertical (power) components is found more representative carrier to system instability resulting from the edge-s random deformation.
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: In this paper, a TSK-type Neuro-fuzzy Inference
System that combines the features of fuzzy sets and neural networks
has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled
Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate
the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on
Particle Swarm Optimization algorithm (PSO) and Genetic
Algorithm (GA).
Abstract: Inventory decisional environment of short life-cycle
products is full of uncertainties arising from randomness and
fuzziness of input parameters like customer demand requiring
modeling under hybrid uncertainty. Prior inventory models
incorporating fuzzy demand have unfortunately ignored stochastic
variation of demand. This paper determines an unambiguous optimal
order quantity from a set of n fuzzy observations in a newsvendor
inventory setting in presence of fuzzy random variable demand
capturing both fuzzy perception and randomness of customer
demand. The stress of this paper is in providing solution procedure
that attains optimality in two steps with demand information
availability in linguistic phrases leading to fuzziness along with
stochastic variation. The first step of solution procedure identifies
and prefers one best fuzzy opinion out of all expert opinions and the
second step determines optimal order quantity from the selected
event that maximizes profit. The model and solution procedure is
illustrated with a numerical example.