A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction

Song, Zhenyu; Tang, Cheng; Ji, Junkai; Todo, Yuki; Tang, Zheng

doi:10.3390/electronics10040373

Cited by 21 publications

(9 citation statements)

References 61 publications

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“…The GBO algorithm has a probability pr of executing LEO, which is set to 0.1. The LEO execution strategy is described in Equation (15).…”

Section: Local Escape Operatormentioning

confidence: 99%

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Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Song

et al. 2022

Electronics

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The logic dendritic neuron model (LDNM), which is inspired by natural neurons, has emerged as a novel machine learning model in recent years. However, recent studies have also shown that the classification performance of LDNM is restricted by the backpropagation (BP) algorithm. In this study, we attempt to use a heuristic algorithm called the gradient-based optimizer (GBO) to train LDNM. First, we describe the architecture of LDNM. Then, we propose specific neuronal structure pruning mechanisms for simplifying LDNM after training. Later, we show how to apply GBO to train LDNM. Finally, seven datasets are used to determine experimentally whether GBO is a suitable training method for LDNM. To evaluate the performance of the GBO algorithm, the GBO algorithm is compared with the BP algorithm and four other heuristic algorithms. In addition, LDNM trained by the GBO algorithm is also compared with five classifiers. The experimental results show that LDNM trained by the GBO algorithm has good classification performance in terms of several metrics. The results of this study indicate that employing a suitable training method is a good practice for improving the performance of LDNM.

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“…The GBO algorithm has a probability pr of executing LEO, which is set to 0.1. The LEO execution strategy is described in Equation (15).…”

Section: Local Escape Operatormentioning

confidence: 99%

“…The BP algorithm uses error backpropagation to adjust the parameter values. However, the BP algorithm is sensitive to the initial values of the parameters and easily falls into local minima [15,16]. Moreover, it is not easy to set the learning rate for BP [17].…”

Section: Introductionmentioning

confidence: 99%

Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Song

et al. 2022

Electronics

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“…Evolutionary algorithms are good choices and have been used in many fields [ 23 , 24 ]. The original SMS algorithm is selected to train the DNM model, which improves the prediction accuracy of the model [ 25 ]. We propose an algorithm based on an innovative population structure selection method (DSMS) for the DNM model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model

Yuan

Gao

Wang

et al. 2023

Neural Comput & Applic

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The rapid industrial development in the human society has brought about the air pollution, which seriously affects human health. PM2.5 concentration is one of the main factors causing the air pollution. To accurately predict PM2.5 microns, we propose a dendritic neuron model (DNM) trained by an improved state-of-matter heuristic algorithm (DSMS) based on STL-LOESS, namely DS-DNM. Firstly, DS-DNM adopts STL-LOESS for the data preprocessing to obtain three characteristic quantities from original data: seasonal, trend, and residual components. Then, DNM trained by DSMS predicts the residual values. Finally, three sets of feature quantities are summed to obtain the predicted values. In the performance test experiments, five real-world PM2.5 concentration data are used to test DS-DNM. On the other hand, four training algorithms and seven prediction models were selected for comparison to verify the rationality of the training algorithms and the accuracy of the prediction models, respectively. The experimental results show that DS-DNM has the more competitive performance in PM2.5 concentration prediction problem.

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“…Deep neural networks have been widely employed in time-series forecasting and prediction problems. The dendritic neural regression (DNR) is one of the promising neural network models that was adopted in time-series forecasting [ 16 , 17 ]. However, DNR faces specific challenges in the parameter configuration, which affects its performance.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

Al-qaness

Ewees

Abualigah

et al. 2022

Entropy

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The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine–cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.

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A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction

Cited by 21 publications

References 61 publications

Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

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