Adopting a dendritic neural model for predicting stock price index movement

Tang, Yajiao; Song, Zhenyu; Zhu, Yulin; Hou, Mian; Tang, Cheng; Ji, Junkai

doi:10.1016/j.eswa.2022.117637

Cited by 18 publications

(8 citation statements)

References 42 publications

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“…The practical applications of DNMs are notably diverse, as demonstrated by Tang et al [ 39 ], who adopted a dendritic neural model for predicting stock price index movement, and Al-qaness et al [ 40 ], who utilized an optimized DNM for wind power forecasting. A comprehensive survey by Ji et al [ 41 ] provides an in-depth look at the mechanisms, algorithms, and practical applications of DNMs, highlighting the extensive research and development in this field.…”

Section: Dendritic Neuron Modelmentioning

confidence: 99%

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies

Wen,

Zhou,

Albeshri

et al. 2024

Sensors

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A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.

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Section: Dendritic Neuron Modelmentioning

confidence: 99%

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies

Wen,

Zhou,

Albeshri

et al. 2024

Sensors

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“…By considering the energy field and relevant data features in detail, we attempt to apply an improved dendritic neuron model to energy-related fields. In our previous work, the effectiveness of the dendritic neuron model was established in various areas, including medical diagnosis [32,33], classification [21,[34][35][36], time series prediction [37][38][39][40], and multiobjective optimization [41,42]. This paper proposes the evolutionary dendritic neural regression (EDNR) model for predicting building EE.…”

Section: Introductionmentioning

confidence: 99%

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Song,

Tang,

Song

et al. 2024

Electronics

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Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.

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“…These dendritic neurons have demonstrated independent information processing capabilities and are well suited for training in combination with evolutionary learning [27]. The DNM contains synaptic, dendritic, membrane, and cell body layers in one model [28,29]. Input data are analyzed by multiple dendrites and subsequently decided by accumulation within the cell whether to output or not.…”

Section: Introductionmentioning

confidence: 99%

Yet Another Effective Dendritic Neuron Model Based on the Activity of Excitation and Inhibition

Yang

Li²,

et al. 2023

Mathematics

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Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies have focused on training DNM using more appropriate algorithms. This paper proposes an improvement to DNM based on the activity of excitation and proposes three new models. Each of the three improved models are designed to mimic the excitation and inhibition activity of neurons. The improved model proposed in this paper is shown to be effective in the experimental part. All three models and original DNM have their own strengths, so it can be considered that the new model proposed in this paper well enriches the diversity of neuronal models and contributes to future research on networks models.

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Adopting a dendritic neural model for predicting stock price index movement

Cited by 18 publications

References 42 publications

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies

Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression

Yet Another Effective Dendritic Neuron Model Based on the Activity of Excitation and Inhibition

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