Application and Evolution for Neural Network and Signal Processing in Large‐Scale Systems

Jia, Dongbao; Li, Cunhua; Li, Qun; Yu, Qin; Meng, Xiangsheng; Zhong, Zhaoman; Ban, Xinxin; Wang, Nizhuan

doi:10.1155/2021/6618833

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…Biogeography-based optimization (BBO) [33][34][35] has been widely applied to simulate ecological concepts, well-known as its high prevision and strong stability using the representative metaheuristics. Particle swarm optimization (PSO) [36,37] has been applied to train neural network instead of BP, whose whole searching and updating process follows the current optimal solution. Unlike genetic algorithm, all particles may converge to the optimal solution faster in most cases, and its advantage of evolutionary computation can deal with some problems of nondifferentiable node transfer function or no gradient information.…”

Section: Introductionmentioning

confidence: 99%

Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

Jia

Liu

et al. 2022

Discrete Dynamics in Nature and Society

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Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision.

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Section: Introductionmentioning

confidence: 99%

Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

Jia

Liu

et al. 2022

Discrete Dynamics in Nature and Society

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“…Different from other neural models, a dendritic neural model considers the nonlinearity of synapse, and it simulates the process of the information transmission of a neuron [17]. Because of its easy explanation and simple implementation, it has been used by many scholars to solve various complex problems, for example, tourism economic forecast [23], bankruptcy prediction [24], breast cancer classification [25], liver disorders [26] and so on [27][28][29][30][31][32][33]. References [25,26] use traditional dendritic neural models with backpropagation algorithm to optimize weights and thresholds.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Dendritic Neural Model for Classification Problems

Jia

Zhong

et al. 2021

Symmetry

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In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed.

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Optimizing the Weights and Thresholds in Dendritic Neuron Model Using the Whale Optimization Algorithm

Dou

et al. 2021

J. Phys.: Conf. Ser.

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In recent years, with the great success of dendritic neuron model (DNM) in various fields, the application of intelligent optimization algorithms in dendritic neuron model has attracted increasing attention of researchers. The training process of neural network is regarded as one of the great challenges of machine learning because of its non-linear nature and unknown optimal parameters. The traditional training algorithm of DNM is prone to fall into local optimum and speed of convergence slowly and so on, resulting in the problem of accuracy and low efficiency. In this paper, for solving the classification problem of dendritic neural model, an innovative intelligent optimization algorithm which named whale optimization algorithm (WOA), is applied to the training of DNM for the first time. Compared with six traditional and classic intelligent optimization algorithms in four classic datasets, the results indicate that WOA-DNM has good performance in various aspects, and its advantage is remarkable.

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Application and Evolution for Neural Network and Signal Processing in Large‐Scale Systems

Cited by 7 publications

References 36 publications

Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

Intelligent Dendritic Neural Model for Classification Problems

Optimizing the Weights and Thresholds in Dendritic Neuron Model Using the Whale Optimization Algorithm

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