نتایج جستجو برای: multiple linear regression pso
تعداد نتایج: 1414892 فیلتر نتایج به سال:
Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the alg...
Purpose: This paper presents an efficient and reliable swarm intelligence-based approach, namely particle swarm optimization [PSO] technique, to optimize the hardness and the parameters that affect the hardness in the Ni-Diamond composite coatings. Design/methodology/approach: Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the i...
Lithium-ion batteries are the current most promising device for electric vehicle applications. They have been widely used because of their advantageous features, such as high energy density, many cycles, and low self-discharge. One critical factors correct operation an is estimation battery charge state. In this sense, work presents a comparison state (SoC), tested in four different conduction ...
A new hybrid PSO-EA-DEPSO algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. This algorithm is shown to outperform RNN predictors trained off-line by PSO, EA, and DEPSO as well as a linear predictor trained by th...
Identifying anomalous values in the realworld database is important both for improving the quality of original data and for reducing the impact of anomalous values in the process of knowledge discovery in databases. Such anomalous values give useful information to the data analyst in discovering useful patterns. Through isolation, these data may be separated and analyzed. The analysis of outlie...
In this paper, we study the mixed linear regression (MLR) problem, where the goal is to recover multiple underlying linear models from their unlabeled linear measurements. We propose a non-convex objective function which we show is locally strongly convex in the neighborhood of the ground truth. We use a tensor method for initialization so that the initial models are in the local strong convexi...
In many real-world statistical problems, we observe a large number of potentially explanatory variables of which a majority may be irrelevant. For this type of problem, controlling the false discovery rate (FDR) guarantees that most of the discoveries are truly explanatory and thus replicable. In this talk, we propose a new method named SLOPE to control the FDR in sparse high-dimensional linear...
Compositional data, containing relative information, occur regularly inmany disciplines and practical situations. Multivariate statistics methods including regression analysis have been adopted to model compositional data, but the existing research is still scattered and fragmented. This paper contributes to modeling the linear regression relationship for compositional data as both dependent an...
BACKGROUND Psoriasis (PsO) is a chronic inflammatory disease with predominantly cutaneous manifestations. Approximately one third of patients with PsO develop psoriatic arthritis (PsA), whereas the remaining proportion of patients has isolated cutaneous psoriasis (PsC). These two phenotypes share common immunology, but with different heredity that might in part be explained by genetic variables...
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