Abstract: In this paper, we consider the MU-MISO downlink scenario, under imperfect channel state information (CSI). The main issue in imperfect CSI is to keep the probability of each user achievable outage rate below the given threshold level. Such a rate outage constraints present significant and analytical challenges. There are many probabilistic methods are used to minimize the transmit optimization problem under imperfect CSI. Here, decomposition based large deviation inequality and Bernstein type inequality convex restriction methods are used to perform the optimization problem under imperfect CSI. These methods are used for achieving improved output quality and lower complexity. They provide a safe tractable approximation of the original rate outage constraints. Based on these method implementations, performance has been evaluated in the terms of feasible rate and average transmission power. The simulation results are shown that all the two methods offer significantly improved outage quality and lower computational complexity.
Abstract: By using fixed point theorems for a class of
generalized concave and convex operators, the positive solution of
nonlinear fractional differential equation with integral boundary
conditions is studied, where n ≥ 3 is an integer, μ is a parameter
and 0 ≤ μ < α. Its existence and uniqueness is proved, and an
iterative scheme is constructed to approximate it. Finally, two
examples are given to illustrate our results.
Abstract: Today, insurers may use the yield curve as an indicator
evaluation of the profit or the performance of their portfolios;
therefore, they modeled it by one class of model that has the ability
to fit and forecast the future term structure of interest rates. This class
of model is the Nelson-Siegel-Svensson model. Unfortunately, many
authors have reported a lot of difficulties when they want to calibrate
the model because the optimization problem is not convex and has
multiple local optima. In this context, we implement a hybrid Particle
Swarm optimization and Nelder Mead algorithm in order to minimize
by least squares method, the difference between the zero-coupon
curve and the NSS curve.
Abstract: The Haussmannization plan of Cairo in 1867 formed a
regular network of roundabout spaces, though deteriorated at present.
The method of identifying the spatial structure of roundabout Cairo
for conservation matches the voronoi diagram with the space syntax
through their geometrical property of spatial convexity. In this
initiative, the primary convex hull of first-order voronoi adopts the
integral and control measurements of space syntax on Cairo’s
roundabout generators. The functional essence of royal palaces
optimizes the roundabout structure in terms of spatial measurements
and the symbolic voronoi projection of 'Tahrir Roundabout' over the
Giza Nile and Pyramids. Some roundabouts of major public and
commercial landmarks surround the pole of 'Ezbekia Garden' with a
higher control than integral measurements, which filter the new
spatial structure from the adjacent traditional town. Nevertheless, the
least integral and control measures correspond to the voronoi
contents of pollutant workshops and the plateau of old Cairo Citadel
with the visual compensation of new royal landmarks on top.
Meanwhile, the extended suburbs of infinite voronoi polygons
arrange high control generators of chateaux housing in 'garden city'
environs. The point pattern of roundabouts determines the
geometrical characteristics of voronoi polygons. The measured
lengths of voronoi edges alternate between the zoned short range at
the new poles of Cairo and the distributed structure of longer range.
Nevertheless, the shortest range of generator-vertex geometry
concentrates at 'Ezbekia Garden' where the crossways of vast Cairo
intersect, which maximizes the variety of choice at different spatial
resolutions. However, the symbolic 'Hippodrome' which is the largest
public landmark forms exclusive geometrical measurements, while
structuring a most integrative roundabout to parallel the royal syntax.
Overview of the symbolic convex hull of voronoi with space syntax
interconnects Parisian Cairo with the spatial chronology of scattered
monuments to conceive one universal Cairo structure. Accordingly,
the approached methodology of 'voronoi-syntax' prospects the future
conservation of roundabout Cairo at the inferred city-level concept.
Abstract: In this paper, the results of Kano from one dimensional
cosine and sine series are extended to two dimensional cosine and sine
series. To extend these results, some classes of coefficient sequences
such as class of semi convexity and class R are extended from
one dimension to two dimensions. Further, the function f(x, y) is
two dimensional Fourier Cosine and Sine series or equivalently it
represents an integrable function or not, has been studied. Moreover,
some results are obtained which are generalization of Moricz’s
results.
Abstract: Sampled-data controller is presented for solid oxide
fuel cell systems which is expressed by a sector bounded nonlinear
model. The proposed control law is obtained by solving a convex
problem satisfying several linear matrix inequalities. Simulation
results are given to show the effectiveness of the proposed design
method.
Abstract: Molluca Collision Zone is located at the junction of
the Eurasian, Australian, Pacific and the Philippines plates. Between
the Sangihe arc, west of the collision zone, and to the east of
Halmahera arc is active collision and convex toward the Molluca Sea.
This research will analyze the behavior of earthquake occurrence in
Molluca Collision Zone related to the distributions of an earthquake
in each partition regions, determining the type of distribution of a
occurrence earthquake of partition regions, and the mean occurence
of earthquakes each partition regions, and the correlation between the
partitions region. We calculate number of earthquakes using partition
method and its behavioral using conventional statistical methods. In
this research, we used data of shallow earthquakes type and its
magnitudes ≥4 SR (period 1964-2013). From the results, we can
classify partitioned regions based on the correlation into two classes:
strong and very strong. This classification can be used for early
warning system in disaster management.
Abstract: This paper proposes a complementary combination scheme of affine projection algorithm (APA) filters with different order of input regressors. A convex combination provides an interesting way to keep the advantage of APA having different order of input regressors. Consequently, a novel APA which has the rapid convergence and the reduced steady-state error is derived. Experimental results show the good properties of the proposed algorithm.
Abstract: The objective of this paper is finding the way of economic restructuring - that is, change in the shares of sectoral gross outputs - resulting in the maximum possible increase in the gross domestic product (GDP) combined with decreases in energy consumption and CO2 emissions. It uses an input-output model for the GDP and factorial models for the energy consumption and CO2 emissions to determine the projection of the gradient of GDP, and the antigradients of the energy consumption and CO2 emissions, respectively, on a subspace formed by the structure-related variables. Since the gradient (antigradient) provides a direction of the steepest increase (decrease) of the objective function, and their projections retain this property for the functions' limitation to the subspace, each of the three directional vectors solves a particular problem of optimal structural change. In the next step, a type of factor analysis is applied to find a convex combination of the projected gradient and antigradients having maximal possible positive correlation with each of the three. This convex combination provides the desired direction of the structural change. The national economy of the United States is used as an example of applications.
Abstract: In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.
Abstract: In this paper an optimal convex controller is designed to control the angle of attack of a FOXTROT aircraft. Then the order of the system model is reduced to a low-dimensional state space by using Balanced Truncation Model Reduction Technique and finally the robust stability of the reduced model of the system is tested graphically by using Kharitonov rectangle and Zero Exclusion Principle for a particular range of perturbation value. The same robust stability is tested theoretically by using Frequency Sweeping Function for robust stability.
Abstract: This paper studies the problem of stability criteria
for neural networks with two additive time-varying delays.A new
Lyapunov-Krasovskii function is constructed and some new delay
dependent stability criterias are derived in the terms of linear
matrix inequalities(LMI), zero equalities and reciprocally convex
approach.The several stability criterion proposed in this paper is
simpler and effective. Finally,numerical examples are provided to
demonstrate the feasibility and effectiveness of our results.
Abstract: In this paper, together with some improved
Lyapunov-Krasovskii functional and effective mathematical
techniques, several sufficient conditions are derived to guarantee the
error system is globally asymptotically stable with H∞
performance, in which both the time-delay and its time variation
can be fully considered. In order to get less conservative results of
the state estimation condition, zero equalities and reciprocally
convex approach are employed. The estimator gain matrix can be
obtained in terms of the solution to linear matrix inequalities. A
numerical example is provided to illustrate the usefulness and
effectiveness of the obtained results.
Abstract: In this paper, the problem of stability criteria for Markovian jumping BAM neural networks with leakage and
discrete delays has been investigated. Some new sufficient condition
are derived based on a novel Lyapunov-Krasovskii functional
approach. These new criteria based on delay partitioning idea are
proved to be less conservative because free-weighting matrices
method and a convex optimization approach are considered. Finally,
one numerical example is given to illustrate the the usefulness and
feasibility of the proposed main results.
Abstract: This paper studies the problem of exponential stability analysis for uncertain neural networks with discrete and distributed time-varying delays. Together with a suitable augmented Lyapunov Krasovskii function, zero equalities, reciprocally convex approach and a novel sufficient condition to guarantee the exponential stability of the considered system. The several exponential stability criterion proposed in this paper is simpler and effective. Finally,numerical examples are provided to demonstrate the feasibility and effectiveness of our results.
Abstract: This paper studies the problem of exponential stability analysis for recurrent neural networks with time-varying delay.By establishing a suitable augmented LyapunovCKrasovskii function and a novel sufficient condition is obtained to guarantee the exponential stability of the considered system.In order to get a less conservative results of the condition,zero equalities and reciprocally convex approach are employed. The several exponential stability criterion proposed in this paper is simpler and effective. A numerical example is provided to demonstrate the feasibility and effectiveness of our results.
Abstract: The The dynamic economic dispatch (DED) problem is one of the complex constrained optimization problems that have nonlinear, con-convex and non-smooth objective functions. The purpose of the DED is to determine the optimal economic operation of the committed units while meeting the load demand. Associated to this constrained problem there exist highly nonlinear and non-convex practical constraints to be satisfied. Therefore, classical and derivative-based methods are likely not to converge to an optimal or near optimal solution to such a dynamic and large-scale problem. In this paper, an Artificial Immune System technique (AIS) is implemented and applied to solve the DED problem considering the transmission power losses and the valve-point effects in addition to the other operational constraints. To demonstrate the effectiveness of the proposed technique, two case studies are considered. The results obtained using the AIS are compared to those obtained by other methods reported in the literature and found better.
Abstract: The dynamic economic dispatch (DED) problem is one of the complex constrained optimization problems that have nonlinear, con-convex and non-smooth objective functions. The purpose of the DED is to determine the optimal economic operation of the committed units while meeting the load demand. Associated to this constrained problem there exist highly nonlinear and non-convex practical constraints to be satisfied. Therefore, classical and derivative-based methods are likely not to converge to an optimal or near optimal solution to such a dynamic and large-scale problem. In this paper, an Artificial Immune System technique (AIS) is implemented and applied to solve the DED problem considering the transmission power losses and the valve-point effects in addition to the other operational constraints. To demonstrate the effectiveness of the proposed technique, two case studies are considered. The results obtained using the AIS are compared to those obtained by other methods reported in the literature and found better.
Abstract: Economic Dispatch is one of the most important power system management tools. It is used to allocate an amount of power generation to the generating units to meet the load demand. The Economic Dispatch problem is a large scale nonlinear constrained optimization problem. In general, heuristic optimization techniques are used to solve non-convex Economic Dispatch problem. In this paper, ideas from Reinforcement Learning are proposed to solve the non-convex Economic Dispatch problem. Q-Learning is a reinforcement learning techniques where each generating unit learn the optimal schedule of the generated power that minimizes the generation cost function. The eligibility traces are used to speed up the Q-Learning process. Q-Learning with eligibility traces is used to solve Economic Dispatch problems with valve point loading effect, multiple fuel options, and power transmission losses.
Abstract: This paper addresses the robust stability problem of a class of delayed neutral Lur’e systems. Combined with the property of convex function and double integral Jensen inequality, a new tripe integral Lyapunov functional is constructed to derive some new stability criteria. Compared with some related results, the new criteria established in this paper are less conservative. Finally, two numerical examples are presented to illustrate the validity of the main results.