Although there are several good books on principal component methods and related
topics, we felt that many of them are either too theoretical or too advanced.
Our goal was to write a practical guide to multivariate analysis, visualization and inter-
pretation, focusing on principal component methods.
The book presents the basic principles of the different methods and provide many exam-
ples in R. This book offers solid guidance in data mining for students and researchers.
Key features
• Covers principal component methods and implementation in R
• Short, self-contained chapters with tested examples that allow for flexibility in
designing a course and for easy reference
At the end of each chapter, we present R lab sections in which we systematically work
through applications of the various methods discussed in that chapter. Additionally,
we provide links to other resources and to our hand-curated list of videos on principal
component methods for further learning.
这本书出自 Richard A. Johnson (Author), Dean W. Wichern (Author) 两位著名的教授，是这个领域比较著名的数，在工业工程，经济管理，工程科技等涉及多变量的领域应用广泛。仅仅用作学习目的，阅后请删除。和csdn上大多数不同的是，我这是清晰版，字都是能选的。
Neural Network Toolbox™ provides algorithms, functions, and apps to create, train,
visualize, and simulate neural networks. You can perform classification, regression,
clustering, dimensionality reduction, time-series forecasting, and dynamic system
modeling and control.
The toolbox includes convolutional neural network and autoencoder deep learning
algorithms for image classification and feature learning tasks. To speed up training of
large data sets, you can distribute computations and data across multicore processors,
GPUs, and computer clusters using Parallel Computing Toolbox™.
This current book provides new research on artificial neural networks (ANNs). Topics
discussed include the application of ANNs in chemistry and chemical engineering fields; the
application of ANNs in the prediction of biodiesel fuel properties from fatty acid constituents;
the use of ANNs for solar radiation estimation; the use of in silico methods to design and
evaluate skin UV filters; a practical model based on the multilayer perceptron neural network
(MLP) approach to predict the milling tool flank wear in a regular cut, as well as entry cut
and exit cut, of a milling tool; parameter extraction of small-signal and noise models of
microwave transistors based on ANNs; and the application of ANNs to deep-learning and
predictive analysis in semantic TCM telemedicine systems.
Chapter 1 - Today, the main effort is focused on the optimization of different processes in
order to reduce and provide the optimal consumption of available and limited resources.
Conventional methods such as one-variable-at-a-time approach optimize one factor at a time
instead of all simultaneously. Unlike this method, artificial neural networks provide analysis
of the impact of all process parameters simultaneously on the chosen responses. The
architecture of each network consists of at least three layers depending on the nature of
process which to be analyzed. The optimal conditions obtained after application of artificial
neural networks are significantly improved compared with those obtained using conventional
methods. Therefore artificial neural networks are quite common method in modeling and
optimization of various processes without the full knowledge about them. For example, one
study tried to optimize consumption of electricity in electric arc furnace that is known as one
of the most energy-intensive processes in industry. Chemical content of scrap to be loaded
and melted in the furnace was selected as the input variable while the specific electricity
consumption was the output variable. Other studies modeled the extraction and adsorption
processes. Many process parameters, such as extraction time, nature of solvent, solid to liquid
ratio, extraction temperature, degree of disintegration of plant materials, etc. have impact on
the extraction of bioactive compounds from plant materials. These parameters are commonly
used as input variables, while the yields of bioactive compounds are used as output during
construction of artificial neural network. During the adsorption, the amount of adsorbent and
adsorbate, adsorption time, pH of medium are commonly used as the input variables, while
the amount of adsorbate after treatment is selected as output variable. Based on the literature
review, it can be concluded that the application of artificial neural networks will surely have
an important role in the modeling and optimization of chemical processes in the future.
