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This article presents a comparison of four nonlinear controllers for positioning tasks of a unicycle-like mobile robot. The controller performance is analyzed through three indexes: IAE, ITAE and IASC. The analysis is performed experimentally. Initially, the controller gains are tuned by simulation until IAE index is less than 5% comparatively. Next, the controllers are run in practice and ITAE and...
Maintaining the balance between convergence and diversity plays a vital role in multi-objective evolutionary algorithms (MOEAs). However, most MOEAs cannot reach a satisfying balance, especially when solving problems having complicated pareto optimal sets. In this paper, we present a modified cooperative co-evolution approach for achieving better convergence and diversity simultaneously (namely DPP2)...
Iterative learning control (ILC) is a simple technique devised for repetitive systems. In this work, an inter-sample ILC algorithm is proposed for the speed control problem of an induction motor. The proposed algorithm is mathematically analyzed and validated using a real-time experiment. Its performance is observed to be better than that of PI based Field Oriented Control (FOC) regarding convergence...
A Getz-Masden dynamic system 2 (GMDS2) is investigated in this paper. For the purpose of solving time-varying complex matrix inverse, we propose a TZ-type (i.e., Taylor-Zhang type) discrete Getz-Masden dynamic system 2; i.e., the proposed TZ-type discrete Getz-Masden dynamic system 2 is discretized by Taylor-Zhang discretization formula. The results of numerical experiments not only show the efficacy...
In this paper, we consider the privacy preserving problem in an agreement network under interception attacks. First, we introduce a consensus protocol with privacy preserving, where each node hides their initial states into a set of random sequences, and then injects the sequences into the process of consensus. Second, we assume that an attacker with limited power can intercept the data transmitted...
With the recent explosion of systems capable of generating and storing large quantities of GPS data, there is an opportunity to develop novel techniques for analyzing and gaining meaningful insights into this spatiotemporal data. In this paper we examine the application of tensor decompositions, a high-dimensional data analysis technique, to georeferenced data sets. Guidance is provided on fitting...
The distributed optimal power flow problem is addressed. No assumptions on the problem cost function, and network topology are needed to solve the optimization problem. A distributed particle swarm optimization algorithm is proposed, based on Deb's rule to handle hard constraints. Moreover, the approach enables to treat a class of distributed optimization problems in which the agents share a common...
We investigate the problem of index coding, where a sender transmits distinct packets over a shared link to multiple users with side information. The aim is to find an encoding scheme (linear combinations) to minimize the number of transmitted packets, while providing each user with sufficient amount of data for the recovery of the desired parts. It has been shown that finding the optimal linear index...
This paper presents novel constrained consensus least mean square (cLMS) algorithms with adjustable constraints that can improve the learning performance of distributed estimation problems in sensor networks by exploiting the spatial diversity of the estimates. For the first algorithm, the constraint vectors are adjusted by combining the components of the estimate orthogonal to its neighbor estimates...
We present some results concerning the representation of unconditionally convergent multipliers, including a reformulation of a conjecture of Balazs and Stoeva.
Recent work has demonstrated the effectiveness of gradient descent for recovering low-rank matrices from random linear measurements in a globally convergent manner. However, their performance is highly sensitive in the presence of outliers that may take arbitrary values, which is common in practice. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against...
Low rank matrix approximation, in the presence of missing data and outliers, has previously shown its significance as a theoretic foundation in a wide spectrum of tabulated information processing applications. To fit low rank models, minimizing the nuclear norm of matrices is a popular scheme, the computational load of which, however, is heavy. While bilinear factorization can largely mitigate the...
The iterative methods are well-known approaches to solve the one-dimensional phase retrieval problem. Amongst them, the error-reduction algorithm is often used since it can easily implement support constraints. Unfortunately this method often stagnates. Recently we have formulated the extended form of the one-dimensional discrete phase retrieval problem and we have assumed that the stagnation can...
The Ethylene cracking is highly nonlinear, complex process with many constraints because of the complex running state of cracking furnace groups. For the solutions to multi-objective problems of yield, cost and benefits, this paper puts forward a biogeography-based multi-objective optimization algorithm with hybrid migration (BBMOHM). The hybrid migration strategy combines the self-adaptive migration...
This paper employs equal-image-size source partitioning techniques to derive the capacities of the general discrete memoryless wiretap channel (DM-WTC) under four different secrecy criteria. These criteria respectively specify requirements on the expected values and tail probabilities of the differences, in absolute value and in exponent, between the joint probability of the secret message and the...
It is shown that when Ankan's n-level polarization transformation is applied to the binary erasure channel, each of the resulting individual 2n subchannels has a sharp threshold, for sufficiently large n.
Convergence of Gibbs fields modeling procedures is evaluated. Evaluation of convergence time is based on analysis of a dynamics of random field realizations space-correlation characteristics during the process of Gibbs fields modeling. Gibbs fields modeling procedure stop time is estimated.
When input dimension of BP neural network increased, the structure of BP neural network will become especially complex and it is vulnerable to fall into local optimum in the training process. Therefore, a new algorithm of BP neural network optimized by rough set (RS) and mind evolutionary algorithm (MEA) is proposed. RS theory was used as the front-end date processing system to achieve attribute reduction,...
In this article, a novel multilevel Monte Carlo (MLMC) simulation approach is applied for large distribution systems reliability evaluation. Basic Monte Carlo simulation (MCS) can be effectively used in this purpose. However, main limitation of MCS is the huge computational cost when a large sample size is needed for a high accuracy. The MLMC method reduces the variance of MCS and speeds up its computational...
In the future, mixed AC and DC grids, spanning multiple areas operated by different transmission system operators (TSO), are expected to offer the necessary controllability for integrating large amounts of intermittent renewable generation. This is facilitated by high voltage direct current transmission based on voltage source converter technology that can offer recourse actions in the form of preventive...
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