Intelligent Systems Modeling and Simulation II : Machine Learning, Neural Networks, Efficient Numerical Algorithm and Statistical Methods (Studies in Systems, Decision and Control)

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Intelligent Systems Modeling and Simulation II : Machine Learning, Neural Networks, Efficient Numerical Algorithm and Statistical Methods (Studies in Systems, Decision and Control)

Abdul Karim, Samsul Ariffin (EDT)

ウェブストア価格 ¥46,151（本体¥41,956）
Springer International Publishing AG（2022/10発売）
外貨定価 US$ 219.99
ポイント 419pt

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製本 Hardcover:ハードカバー版／ページ数 695 p.
言語 ENG
商品コード 9783031040276
DDC分類 511.8

Full Description

This book develops a new system of modeling and simulations based on intelligence system. As we are directly moving from Third Industrial Revolution (IR3.0) to Fourth Industrial Revolution (IR4.0), there are many emergence techniques and algorithm that appear in many sciences and engineering branches. Nowadays, most industries are using IR4.0 in their product development as well as to refine their products. These include simulation on oil rig drilling, big data analytics on consumer analytics, fastest algorithm for large-scale numerical simulations and many more. These will save millions of dollar in the operating costs. Without any doubt, mathematics, statistics and computing are well blended to form an intelligent system for simulation and modeling. Motivated by this rapid development, in this book, a total of 41 chapters are contributed by the respective experts. The main scope of the book is to develop a new system of modeling and simulations based on machine learning, neural networks, efficient numerical algorithm and statistical methods. This book is highly suitable for postgraduate students, researchers as well as scientists that have interest in intelligent numerical modeling and simulations.

Introduction.- Data-driven Ordinary Differential Equations Model for predicting missing data and forecasting Crude Oil Prices.- Efficient iterative approximation for nonlinear porous medium equation with drainage model.- The Performance of Logistic Regression and Discriminant Analysis in Spam E-mail Classification.- Detecting Structural Breaks and Outliers for Volatility Data via Impulse Indicator Saturation.- Index.