scholarly journals Precipitation forecast of the Wujiang River Basin based on artificial bee colony algorithm and backpropagation neural network

2020 ◽  
Vol 59 (3) ◽  
pp. 1473-1483
Author(s):  
Yongtao Wang ◽  
Jian Liu ◽  
Rong Li ◽  
Xinyu Suo ◽  
Enhui Lu
Keyword(s):  
Neural Network ◽  
River Basin ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Precipitation Forecast ◽  
Backpropagation Neural Network ◽  
Bee Colony ◽  
Wujiang River

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

scholarly journals Inferring Gene Regulatory Network with Recurrent Neural Network and Extended Artificial Bee Colony Algorithm

2018 ◽  
Vol 28 (2) ◽  
pp. 237-250
Author(s):  
Tausif Al Hossain ◽  
Mohammad Shoyaib ◽  
Saifuddin Md Tareeq
Keyword(s):  
Neural Network ◽  
Gene Regulatory Network ◽  
Recurrent Neural Network ◽  
Regulatory Network ◽  
Artificial Bee Colony Algorithm ◽  
Local Minimum ◽  
Artificial Bee Colony ◽  
Computational Techniques ◽  
Bee Colony ◽  
Gene Regulatory

Gene regulatory network is the network of genes interacting with each other performing as functional circuitry inside a cell. Many cellular processes are controlled by this network as they govern the expression levels of genes or gene product. High performance computational techniques are needed to analyze these data as it is heavily affected by noise. There are a number of algorithms available in the literature which use recurrent neural network for model building together with differential evolution, particle swarm optimization or genetic algorithm for searching the regulatory network. The problem with these methods is that they may trap in the local minimum. In this paper, we present an algorithm using recurrent neural network as model and an extended artificial bee colony algorithm for searching regulatory network that can avoid local minimum. A comprehensive analysis on both artificial and real data shows the effectiveness of the proposed approach. Furthermore we have also varied the network dimension and the noise level present in gene expression profiles. The reconstruction method has successfully predicted the underlying network topology while maintaining high accuracy. The proposed approach has also been applied to the real expression data of SOS DNA repair system in Escherichia coli and successfully predicted important regulations. Plant Tissue Cult. & Biotech. 28(2): 237-250, 2018 (December)

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