Evolutionary Dendritic Neural Model for Classification Problems

Qian, Xiaoxiao; Tang, Cheng; Todo, Yuki; Lin, Qiuzhen; Ji, Junkai

doi:10.1155/2020/6296209

Cited by 9 publications

(4 citation statements)

References 54 publications

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“…However, the dendritic neuron model faces certain limitations in the parameter configuration process. To this end, in recent years, advances in metaheuristic (MH) optimization algorithms have been adopted to boost the performance of the dendritic neuron model by training and optimizing its parameters, as in the genetic algorithm [34], the cuckoo search (CS) algorithm [37], and particle swarm optimization [38].…”

Section: Motivation and Contributionsmentioning

confidence: 99%

Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

et al. 2022

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It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets.

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Section: Motivation and Contributionsmentioning

confidence: 99%

Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

et al. 2022

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“…In addition, the simplified topology can be implemented by the logic circuits, which demonstrates that the DNM can obtain excellent performance by consuming only little computing resources, with easy hardware implementation [36]. The efficiency of the DNM is certainly promising in the era of big data, which brings to a climax the studies on improvement [37][38][39][40]. The performance of the DNMs has been proven in various fields, such as computer-aided diagnosis [41], bankruptcy prediction [42], wind speed forecasting [43], and PM2.5 concentration prediction [44].…”

Section: Introductionmentioning

confidence: 99%

A Hardware-Based Orientation Detection System Using Dendritic Computation

Nomura,

Chen,

Tang

et al. 2024

Electronics

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Studying how objects are positioned is vital for improving technologies like robots, cameras, and virtual reality. In our earlier papers, we introduced a bio-inspired artificial visual system for orientation detection, demonstrating its superiority over traditional systems with higher recognition rates, greater biological resemblance, and increased resistance to noise. In this paper, we propose a hardware-based orientation detection system (ODS). The ODS is implemented by a multiple dendritic neuron model (DNM), and a neuronal pruning scheme for the DNM is proposed. After performing the neuronal pruning, only the synapses in the direct and inverse connections states are retained. The former can be realized by a comparator, and the latter can be replaced by a combination of a comparator and a logic NOT gate. For the dendritic function, the connection of synapses on dendrites can be realized with logic AND gates. Then, the output of the neuron is equivalent to a logic OR gate. Compared with other machine learning methods, this logic circuit circumvents floating-point arithmetic and therefore requires very little computing resources to perform complex classification. Furthermore, the ODS can be designed based on experience, so no learning process is required. The superiority of ODS is verified by experiments on binary, grayscale, and color image datasets. The ability to process data rapidly owing to advantages such as parallel computation and simple hardware implementation allows the ODS to be desirable in the era of big data. It is worth mentioning that the experimental results are corroborated with anatomical, physiological, and neuroscientific studies, which may provide us with a new insight for understanding the complex functions in the human brain.

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“…Apart from the development of medical aid applications, an unconventional method was also applied to the financial field. To improve the classification performance, metaheuristic algorithms were introduced to train the hyperparameters of DNM [ 22 , 23 ]. Through the use of the decision tree, Luo et al initialized the model to realize better effectiveness [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

An Extension Network of Dendritic Neurons

Peng

Gao

Wang

et al. 2023

Computational Intelligence and Neuroscience

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Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch–Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability. In this study, a novel extended network based on the dendritic structure is innovatively proposed, thereby enabling it to solve multiple-class classification problems. Also, for the first time, an efficient error-back-propagation learning algorithm is derived. In the extensive experimental results, the effectiveness and superiority of the proposed method in comparison with other nine state-of-the-art classifiers on ten datasets are demonstrated, including a real-world quality of web service application. The experimental results suggest that the proposed learning algorithm is competent and reliable in terms of classification performance and stability and has a notable advantage in small-scale disequilibrium data. Additionally, aspects of network structure constrained by scale are examined.

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Evolutionary Dendritic Neural Model for Classification Problems

Cited by 9 publications

References 54 publications

Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

A Hardware-Based Orientation Detection System Using Dendritic Computation

An Extension Network of Dendritic Neurons

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