Metaheuristic algorithms have gained popularity in the past decade due to their remarkable ability to address various optimization challenges. Among these, the JAYA algorithm has emerged as a recent contender that dem...
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Metaheuristic algorithms have gained popularity in the past decade due to their remarkable ability to address various optimization challenges. Among these, the JAYA algorithm has emerged as a recent contender that demonstrates strong performance across different optimization problems, largely attributed to its simplicity. However, real-world problems have become increasingly complex in today's era, creating a demand for more robust and effective solutions to tackle these intricate challenges and achieve outstanding results. This article proposes an enhanced JAYA (EJAYA) method that addresses its inherent shortcomings, resulting in improved convergence and search capabilities when dealing with diverse problems. The current study evaluates the performance of the proposed optimization methods on both continuous and discontinuous problems. Initially, EJAYA is applied to solve 20 prominent test functions and is validated by comparison with other contemporary algorithms in the literature, including moth-flame optimization, particle swarm optimization, the dragonfly algorithm, and the sine-cosine algorithm. The effectiveness of the proposed approach in discrete scenarios is tested using feature selection and compared to existing optimization strategies. Evaluations across various scenarios demonstrate that the proposed enhancements significantly improve the JAYA algorithm's performance, facilitating escape from local minima, achieving faster convergence, and expanding the search capabilities.
The multilayer perceptrons (MLPs) have been widely used in many communication applications, however, the learning process of the multilayer perceptrons often becomes very slow, which is due to the existence of the sin...
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The multilayer perceptrons (MLPs) have been widely used in many communication applications, however, the learning process of the multilayer perceptrons often becomes very slow, which is due to the existence of the singularities in the parameter space. As the singularities significantly affect the learning dynamics of MLPs, the standard gradient descent method is not Fisher efficient. In order to overcome this problem, natural gradient method and adaptive natural gradient method were proposed to accelerate the learning process. As is well known, step size in each iteration plays a key role in the performance of algorithms. In this paper, the modified adaptive natural gradient method is proposed where the step size in each iteration is adaptive modified. The aim of the proposed algorithm is to accelerate the convergence speed and increase the performance of MLPs. The simulation results verify the validity of the analytical results.
In this paper,an unsupervised learning algorithm called the modified fuzzy min-max neural network for clustering on the application of the pipeline internal inspection data(MFNNC) is *** the original fuzzy min-max clu...
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ISBN:
(纸本)9781509009107
In this paper,an unsupervised learning algorithm called the modified fuzzy min-max neural network for clustering on the application of the pipeline internal inspection data(MFNNC) is *** the original fuzzy min-max clustering algorithm,each cluster of the MFNNC is a *** the hyperbox is decided by its membership *** size of the cluster is determined by its minimum point and maximum *** with FMNN by Simpson(1993),the MFNNC has stronger robustness and higher accuracy,which has proposed an boundary rule and also taken the noise into *** the MFNNC,the problem of the points on the contraction boundary has been *** the influence of noise on the whole algorithm is *** performance of the MFNNC is checked by the IRIS data *** simulation result shows that the MFNNC has better performance than the *** last,the application on the oil pipeline is *** result shows that our modified algorithm scheme can be regarded as a method to preprocess for the classification of the pipeline internal inspection data.
The integral-free Kalman filters which are widely used in target tracking are studied. The algorithms of Unscented Kalman filter (UKF), Cubature Kalman filter (CKF) and Square-root cubature Kalman filter (SCKF) are co...
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ISBN:
(纸本)9781479958368
The integral-free Kalman filters which are widely used in target tracking are studied. The algorithms of Unscented Kalman filter (UKF), Cubature Kalman filter (CKF) and Square-root cubature Kalman filter (SCKF) are compared in details. A modified algorithm (MSCKF) is proposed to optimize the performance. When considering different original ranges and radial speeds, simulation of linear motion targets in Gauss noise is made and their tracking errors are analyzed. The result shows that different tracking filter algorithm has respective features in short time and long range signal processing. The MSCKF has better tracking performance in short time tracking application. It offers the guideline for application.
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