In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection metho...
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In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salpswarmalgorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality.
The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without a...
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Epilepsy is a brain disorder characterized by sudden seizures, periodic abnormal and inappropriate behaviour, and an altered state of consciousness. The visual diagnosis of epilepsy using electroencephalogram (EEG) si...
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Epilepsy is a brain disorder characterized by sudden seizures, periodic abnormal and inappropriate behaviour, and an altered state of consciousness. The visual diagnosis of epilepsy using electroencephalogram (EEG) signals is challenging, which led to the development of machine learning methods to automate this task. With the help of machine learning techniques optimized for epilepsy, this study aims to diagnose epilepsy disorders and related seizures with high accuracy. In the first step, the proposed multilevel method applies Discrete Wavelet Transform (DWT) to decompose the EEG signal into sub-band frequency levels. Next, the algorithm uses the Modified binary salp swarm algorithm (MBSSA), a population-based strategy, to extract time-domain features. The proposed method uses the Levenberg-Marquardt (LM) backpropagation classification model as a Feed-Forward Neural Network (FFNN). The MBSSA also optimally determine the type of WT (DWT or Double Density DWT (DDT)), the number of decomposed levels in WT, the best-fitted mother wavelet and the number of neurons in the hidden layer of FFNN, preventing manual and time-consuming calculation. Evaluation procedures compare the proposed method to other state-of-the-art methods and verify its superiority by achieving the highest and average accuracy of %99.45 and %98.46, respectively.
In this paper, a multiple target localization algorithm based on compressive sensing reconstruction of binary salp swarm algorithm (BSSA) is proposed to improve the multi-target positioning accuracy and anti-noise in ...
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ISBN:
(纸本)9781728140940
In this paper, a multiple target localization algorithm based on compressive sensing reconstruction of binary salp swarm algorithm (BSSA) is proposed to improve the multi-target positioning accuracy and anti-noise in wireless sensor networks. The continuous salpswarmalgorithm is discretized in the binary space, and the essential characteristics of the rapid coordination change and foraging of the salpswarm are preserved, and then used for the reconstruction of compressive sensing signals to achieve multi-target positioning under the wireless sensor networks. The experimental results shows that compared with the traditional compressive sensing reconstruction algorithm, the algorithm has good noise immunity and counting performance. The positioning performance is better than the greedy matching pursuit(GMP) algorithm and the traditional l(1)-norm minimization algorithm.
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