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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
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Authors (2):
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Pavan Kumar Kolluru Pavan Kumar Kolluru
Author Profile Icon Pavan Kumar Kolluru
Pavan Kumar Kolluru
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with MATLAB Machine Learning FREE CHAPTER 2. Importing and Organizing Data in MATLAB 3. From Data to Knowledge Discovery 4. Finding Relationships between Variables - Regression Techniques 5. Pattern Recognition through Classification Algorithms 6. Identifying Groups of Data Using Clustering Methods 7. Simulation of Human Thinking - Artificial Neural Networks 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction 9. Machine Learning in Practice

Data fitting for predicting the quality of concrete

Concrete is the most important material in civil engineering. Its compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. The test to calculate compressive strength is carried out either on a concrete cube or cylinder through the use of a compression testing machine (2000 kN). The test is destructive and takes a long time, so the possibility of predicting the compressive strength takes on a significant importance. The following figure shows a compressive strength test:

Figure 9.1: The concrete compressive strength test

In this study we want to create a model that allows us to calculate the compressive strength according to the ingredients used in the mixture. We begin, as always, by...

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