Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm

被引：0
作者：
Ahmet Cevahir Cinar
机构：
[1] Selcuk University,Department of Computer Engineering, Faculty of Technology
来源：
Arabian Journal for Science and Engineering | 2020年 / 45卷
关键词：
Tree-seed algorithm; Multi-layer perceptron; Training neural network; Artificial neural network; Neural networks; Nature inspired algorithms;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The artificial neural network (ANN) is the most popular research area in neural computing. A multi-layer perceptron (MLP) is an ANN that has hidden layers. Feed-forward (FF) ANN is used for classification and regression commonly. Training of FF MLP ANN is performed by backpropagation (BP) algorithm generally. The main disadvantage of BP is trapping into local minima. Nature-inspired optimizers have some mechanisms escaping from the local minima. Tree-seed algorithm (TSA) is an effective population-based swarm intelligence algorithm. TSA mimics the relationship between trees and their seeds. The exploration and exploitation are controlled by search tendency which is a peculiar parameter of TSA. In this work, we train FF MLP ANN for the first time. TSA is compared with particle swarm optimization, gray wolf optimizer, genetic algorithm, ant colony optimization, evolution strategy, population-based incremental learning, artificial bee colony, and biogeography-based optimization. The experimental results show that TSA is the best in terms of mean classification rates and outperformed the opponents on 18 problems.
引用
收藏
页码：10915 / 10938
页数：23
相关论文
共 130 条
  • [11] Qian X(2019)Median-Pi artificial neural network for forecasting Neural Comput. Appl. 31 307-316
  • [12] Shojaeifard A(2015)Classification of leaf type using artificial neural networks Int. J. Intell. Syst. Appl. Eng. 3 136-139
  • [13] Amroudi AN(2016)Regularized neural networks fusion and genetic algorithm based on-field nitrogen status estimation of wheat plants IEEE Trans. Industr. Inf. 13 103-114
  • [14] Mansoori A(2018)Building a globally optimized computational intelligent image processing algorithm for on-site inference of nitrogen in plants IEEE Intell. Syst. 33 15-26
  • [15] Erfanian M(2019)Multi-view temporal ensemble for classification of non-stationary signals IEEE Access 7 32482-32491
  • [16] Tavanaei A(2018)Designing high-resolution time–frequency and time–scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance Digital Signal Process. 77 120-152
  • [17] Ghodrati M(1997)No free lunch theorems for optimization IEEE Trans. Evol. Comput. 1 67-82
  • [18] Kheradpisheh SR(2012)Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm Appl. Math. Comput. 218 11125-11137
  • [19] Masquelier T(2014)Let a biogeography-based optimizer train your multi-layer perceptron Inf. Sci. 269 188-209
  • [20] Maida A(2018)A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training Neural Comput. Appl. 30 3707-3720
12345678910