Control of constrained high dimensional nonlinear liquid level processes using a novel neural network based Rapidly exploring Random Tree algorithm

2020 ◽  
Vol 96 ◽  
pp. 106709
Author(s):  
B. Jaganatha Pandian ◽  
Mathew Mithra Noel
Keyword(s):  
Neural Network ◽  
Random Tree ◽  
Liquid Level ◽  
High Dimensional ◽  
Tree Algorithm

scholarly journals Path planning for mobile robot based on modified rapidly exploring random tree method and neural network

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878422 ◽  
Author(s):  
Pengchao Zhang ◽  
Chao Xiong ◽  
Wenke Li ◽  
Xiaoxiong Du ◽  
Chuan Zhao
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Random Tree ◽  
Target Point ◽  
Planning Problem ◽  
Tree Algorithm ◽  
Time Performance ◽  
Path Planning Problem

In the course of the task, the mobile robot should find the shortest and most smooth obstacle-free path to move from the current point to the target point efficiently, which is namely the path planning problem of the mobile robot. After analyzing a large number of planning algorithms, it is found that the combination of traditional planning algorithm and heuristic programming algorithm based on artificial intelligence have outstanding performance. Considering that the basic rapidly exploring random tree algorithm is widely used for some of its advantages, meanwhile there are still defects such as poor real-time performance and rough planned path. So, in order to overcome these shortcomings, this article proposes target bias search strategy and a new metric function taking both distance and angle into account to improve the basic rapidly exploring random tree algorithm, and the neural network is used for curve post-processing to obtain a smooth path. Through simulating in complex environment and comparison with the basic rapidly exploring random tree algorithm, it shows good real-time performance and relatively shorter and smoother planned path, proving that the improved algorithm has good performance in handling path planning problem.

scholarly journals Building a Classifier of Onset Stroke Prediction Using Random Tree Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 61-66 ◽  
Author(s):  
Yu-Chen Chen ◽  
◽  
Takashi Suzuki ◽  
Masaaki Suzuki ◽  
Hiroyuki Takao ◽  
...  
Keyword(s):  
Random Tree ◽  
Tree Algorithm

scholarly journals A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 82-96 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Sanja Pfeifer ◽  
Nataša Šarlija
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Classification Problem ◽  
High Dimensional ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dimensional Classification ◽  
Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

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