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The damped pseudo-transient analysis (DPTA) method, of step 1 up to 3 are described as they are applied to a nonlinear circuit. The DPTA method with an effective implementation based on SPICE3 is compared to the pure pseudo-transient analysis and compound element pseudo-transient analysis (CEPTA) methods, and is shown to be more efficient. The first order multi-step linear integration algorithms,...
In this paper, we propose a geometric algorithm for interpolating a given polygon using non-uniform cubic Bsplines. Geometric interpolation uses the given polygon as the initial shape of the control polygon of the B-spline and reduces the approximate error by iteratively updating the control points with the deviations from the corresponding interpolated vertices to their nearest foot points on the...
In this paper, we derive a new iterative method of order five to solve the system of nonlinear equations. The new method is based on the two-step iterative method of order three proposed by Sharma and Gupta in [5]. The efficiency index is used to compare the efficiency of the new method with those of other ones. Numerical examples are given to show that the proposed method has higher efficiency than...
In order to escape from premature convergence and improve the efficiency of the Quantum-behaved particle swarm optimization (QPSO) algorithm, this paper propose a new algorithm PDCQPSO, which employing diversity-controlled mechanism into QPSO to increase the diversity of population and parallel technique to shorten the running time of algorithm. A comprehensive experimental study is conducted on a...
Metabolic flux estimation through 13C trace experiment is crucial for metabolic system to quantify the intracellular metabolic fluxes. In essence, it corresponds to a constrained optimization problem, objective function of which is non-linear and non-differentiable and exist multiple local minima making this problem a special difficulty. In this paper, we propose Quantum-behaved particle swarm optimization...
Q-learning is a one of the best model-free reinforcement learning algorithms. The goal is to find an estimate of the optimal action-value function called Q-value function. The Q-value function is defined as the expected sum of future rewards obtained by taking an action in the current state. The main drawback of Q-learning is that the learning process is expensive for the agent, specially, in the...
This paper proposes a Simulated Annealing based Global Maximum Power Point Tracking (GMPPT) technique designed for photovoltaic (PV) systems which experience partial shading conditions (PSC). The proposed technique is compared with the common Perturb and Observe MPPT technique and the Particle Swarm Optimization approach to GMPPT. The performance is assessed by considering the time taken to converge...
D-S evidence theory has been widely used in various fields of information fusion due to its efficiency in dealing with uncertain information. Unfortunately, combination of conflicting evidences with the classical Dempster's rule may produce the counter-intuitive results. In this paper, Definitions of correlation coefficient and credibility are first presented, followed by the introduction of evidence...
Artificial three-dimensional (3D) video technology has generated significant interest in recent years. However, there are concerns about side effects such as eye strain that may result from viewing 3D video images. This visual fatigue is assumed to be related to a mismatch between convergence distance and focal length that occurs while viewing 3D content. In this study, we investigated the relationship...
The function of depth perception may be related to the ability to perform vergence eye movements during the viewing of three-dimensional stereoscopic movies.
Image restoration can be attributed to solving a linear systems and conjugate gradient method is an effective iteration algorithm for solving various linear systems. However the convergence rate of CGM is determined by condition number of coefficient matrix. The level1 and level2 preconditioner were used to reduce the condition number of coefficient matrix and to accelerate the convergence rate. Simulation...
We consider the problem of optimizing a sum of local objective functions corresponding to multiple agents. We discuss a distributed model where the agents can only exchange quantization data over a time-varying network. For solving this problem, we propose a method that involves agents updating their states by weighted averaging and probabilistically quantized information. The method indicates how...
The question put forward in this paper is whether robots can create conformity by means of group pressure. We recreate and expand on a classic social psychology experiment by Solomon Asch, so as to explore three main dimensions. First, we wanted to know whether robots can prompt conformity in human subjects, and whether there is a significant difference between the degree to which individuals conform...
To tackle with problems emerging with rapid growth of information networks in scale and complexity, selforganization is one of promising design principles for future networks. Convergence of self-organizing controls, however, is pointed out to be comparatively slow in some practical applications. Therefore, it is important to reveal and enhance convergence of self-organizing controls. In controlled...
A novel fault approximation tool (FAT) is presented for use in distribution networks with a high penetration of inverter-interfaced distributed generation. The FAT does not rely on balanced operation nor an absolute reference point for voltage calculations, both of which are required for Newton-Raphson load flow calculations. Simulations show that the FAT provides an accurate representation of the...
This paper proposes a proportionate affine projection algorithm based on coefficient difference (DPAPA). Its adaptation gain for each tap is proportional to the absolute value of the difference between the current tap weight estimate and the previous tap weight estimate. In addition, a simple version is introduced to decrease the amount of calculation. The simulation results show that the proposed...
Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC algorithm, which is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. For these insufficiencies,...
The theory and applications of local transparent boundary conditions (TBCs) is a topic of current interest. The Hardy space infinite element (HSIE) method for the scalar Helmholtz equation presented in [1] is one such method, and has been applied to the curl-curl form of the Maxwell equations within a conforming finite element method [2]. In this paper we describe a reformulation of the HSIE method...
The online solution of optimization (including minimization and maximization) is viewed as a basic and important issue, which has been widely arisen in scientific researches and engineering applications. In this paper, a new recurrent neural network (NRNN) is generalized and investigated for the nonlinear optimization problem. In addition, two gradient neural networks are employed for comparison....
This paper proposes a new particle swarm optimization method that use chaotic maps for parameter adaptation. To enhance the performance of particle swarm optimization, which is an evolutionary computation technique through individual improvement plus population cooperation and competition, a modified particle swarm optimization algorithm is proposed by incorporating chaos(CPSO). Firstly, diversity...
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