Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm

Lu, Jinna; Hu, Hongping; Bai, Yanping

doi:10.1016/j.neucom.2014.10.065

Cited by 71 publications

(25 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [39], the Adaboost-BPNN model was used to predict the market demand for refrigerator, which reflected good prediction performance of this model. Besides, Lu and Hu et al [40] developed a new time series prediction method combining the Adaboost algorithm and generalized radial basis function neural network (GRBF), and actual examples demonstrated that the proposed model was effective and feasible for prediction problems. The increasing application of Adaboost algorithm can be attributed to two aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Zhou

et al. 2019

Journal of Energy

View full text Add to dashboard Cite

As a pillar industry of national economy, China’s construction industry is still facing the status of substantial energy consumption and high CO2 emissions, which is a key field of energy conservation and emission reduction. In CO2 emissions research, it is essential to focus on analyzing the present and future trends of CO2 emissions in China’s construction industry. This article introduces a novel prediction model, in which the weighted algorithm is combined with Elman neural network (ENN) optimized by Adaptive Boosting algorithm (Adaboost) for evaluating future CO2 emissions in China’s construction industry. Firstly, logarithmic mean Divisia index (LMDI) is used to decompose CO2 emissions into economy, structural, intensity, and population indicators, posing as inputs to the weighted Adaboost-ENN model. Then, through comparison with other three models based on the data of total CO2 emissions in China’s construction industry during 2004-2016, there is evidence that the proposed model makes a favorable prediction performance. On this basis, we employ scenario analysis to predict future trend of CO2 emissions in China’s construction industry. It can be found that the peak of CO2 emissions in China’s construction industry will be achieved before 2030 in high carbon scenario (HS) and baseline carbon scenario (BS), whereas it will not be realized in low carbon scenario (LS). Finally, the specific policy recommendations related to energy conservation and emission reduction in China’s construction industry are proposed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Zhou

et al. 2019

Journal of Energy

View full text Add to dashboard Cite

show abstract

“…The RBFNN has Gaussian functions as its hidden neurons. The GRBFNN is a modified RBFNN and adopts the generalized Gaussian functions as its hidden neurons [43,44]. The output of the GRBFNN can be expressed as [43,44] …”

Section: Generalized Radial Basis Function Neural Networkmentioning

confidence: 99%

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

Ding

et al. 2018

Energies

View full text Add to dashboard Cite

Abstract:To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methods-the backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction.

show abstract

“…For example, SCA and GA are used to optimize the weight and basis of artificial neural network for predicting the direction of stock market index, respectively [31,32]. An improved dynamic particle swarm optimization with AdaBoost algorithm is used to optimize the parameters of generalized radial basis function neural network for stock market prediction [33], and an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm is utilized to optimize the parameters of radial basis function neural network for the air quality index (AQI) prediction [34], respectively. Artificial tree (AT) algorithm was improved and applied to optimize the parameters of artificial neural network for predicting influenza-like illness [35].…”

Section: Introductionmentioning

confidence: 99%

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Bai

et al. 2019

Complexity

Self Cite

View full text Add to dashboard Cite

The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO-BPNN, which is against the models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN, MVO-BPNN, and ALO-BPNN. It is shown that the predicted model IALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

show abstract

Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm

Cited by 71 publications

References 27 publications

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Contact Info

Product

Resources

About

Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm

Cited by 71 publications

References 27 publications

Forecasting CO2 Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Forecasting CO2 Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Contact Info

Product

Resources

About

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis

Forecasting CO₂ Emissions in China’s Construction Industry Based on the Weighted Adaboost-ENN Model and Scenario Analysis