Preface

Chapter 9 **Breast Cancer Detection by Means of Artificial Neural**

Moreira Galvan and Jorge Alberto Barrios Garcia

Chapter 10 **Applications of Artificial Neural Networks in Biofuels 181**

Chapter 11 **ANN Modelling to Optimize Manufacturing Process 201**

Chapter 12 **Artificial Neural Networks (ANNs) for Spectral Interference**

Z. Li, X. Zhang, G. A. Mohua and Vassili Karanassios

Rosangela Saher Cintra and Haroldo F. de Campos Velho

**Deep Learning for a Miniature One 227**

Chapter 13 **Solar Radiation Prediction Using NARX Model 251** Ines Sansa and Najiba Mrabet Bellaaj

Chapter 14 **Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model 265**

Jose Manuel Ortiz-Rodriguez, Carlos Guerrero-Mendez, Maria del Rosario Martinez-Blanco, Salvador Castro-Tapia, Mireya Moreno-Lucio, Ramon Jaramillo-Martinez, Luis Octavio Solis-Sanchez, Margarita de la Luz Martinez-Fierro, Idalia Garza-Veloz, Jose Cruz

Alex Oliveira Barradas Filho and Isabelle Moraes Amorim Viegas

Luigi Alberto Ciro De Filippis, Livia Maria Serio, Francesco Facchini

**Correction Using a Large-Size Spectrometer and ANN-Based**

**Networks 161**

**VI** Contents

and Giovanni Mummolo

This book addresses recent advances and multidisciplinary advanced applications of artifi‐ cial neural networks (ANNs) in various fields. Artificial neural network technique is one of the most important techniques to help researchers for solving a wide area of problems. This book supplies the reader with an integrative view of latest research based on ANN. It will help graduates, senior-level undergraduate students, professionals, and researchers to learn foundational and advanced topics of ANN technique. It presents both theoretical knowl‐ edge and hands-on work from multidisciplinary experts. ANN is a computational structure inspired by a biological nervous system. ANN consists of very simple and highly intercon‐ nected processors called neurons. The neurons are connected to each other by weighted links over which signals can pass. Each neuron receives multiple inputs from other neurons in proportion to its connection weights and generates a single output that may propagate to several other neurons. The process consists of data collection, analysis and processing, net‐ work structure design, number of hidden layers, number of hidden units, initializing, train‐ ing the network, network simulation, weights/bias adjustments, and testing the network.

In this book, highly qualified scientists in this field grasp the most recent researches motivat‐ ed by the importance of artificial neural networks. The chapters selected for this book are to reflect current technologies, new concepts, advanced applications, and methods related to the book's topic from different perspectives. This book introduces innovative case studies for the next-generation optical networks based on modulation recognition using artificial neural networks, hardware ANN for gait generation of multilegged robots, production of high-resolution soil property ANN maps, ANN and dynamic factor models to combine fore‐ casts, ANN parameter recognition of engineering constants in Civil Engineering, ANN elec‐ tricity consumption and generation forecasting, ANN for advanced process control, ANN breast cancer detection, ANN applications in biofuels, ANN modeling for manufacturing process optimization, spectral interference correction using a large-size spectrometer and ANN-based deep learning, solar radiation ANN prediction using NARX model, and ANN data assimilation for an atmospheric general circulation model. These advanced ANN appli‐ cations are well prepared and presented in the form of 14 chapters as follows:

Chapter (1): Introductory Chapter: Artificial Neural Networks

Chapter (2): Modulation Format Recognition Using Artificial Neural Networks for the Next-Generation Optical Networks

Chapter (3): Gait Generation of Multilegged Robots by Using Hardware Artificial Neural Networks

Chapter (4): Using Artificial Neural Networks to Produce High-Resolution Soil Property Maps

Chapter (5): Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models?

Chapter (6): Parameter Recognition of Engineering Constants of CLSMs in Civil Engineering Using Artificial Neural Networks

Chapter (7): Electricity Consumption and Generation Forecasting with Artificial Neural Net‐ works

Chapter (8): Advanced Process Control

Chapter (9): Breast Cancer Detection by Means of Artificial Neural Networks

Chapter (10): Applications of Artificial Neural Networks in Biofuels

Chapter (11): ANN Modeling to Optimize Manufacturing Process

Chapter (12): Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

Chapter (13): Solar Radiation Prediction Using NARX Model

Chapter (14): Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model

This book is dedicated to the victims of the last terrorism accidents in Egypt (my home country). Surely, this cancer will disappear from the world sooner than later.

Best Regards, Adel

#### **Dr. Adel El-Shahat, Senior IEEE Member**

Assistant Professor, Department of Electrical Engineering, Founder & Director of Innovative Power Electronics & Nano-Grids Research Lab. (IPENG), Georgia Southern University, Statesboro, Georgia, USA

#### **Introductory Chapter: Artificial Neural Networks** Introductory Chapter: Artificial Neural Networks

DOI: 10.5772/intechopen.73530

#### Adel El-Shahat Adel El-Shahat

Chapter (5): Dynamic Factor Model and Artificial Neural Network Models: To Combine

Chapter (6): Parameter Recognition of Engineering Constants of CLSMs in Civil Engineering

Chapter (7): Electricity Consumption and Generation Forecasting with Artificial Neural Net‐

Chapter (12): Artificial Neural Networks (ANNs) for Spectral Interference Correction Using

Chapter (14): Data Assimilation by Artificial Neural Networks for an Atmospheric General

This book is dedicated to the victims of the last terrorism accidents in Egypt (my home

Founder & Director of Innovative Power Electronics & Nano-Grids Research Lab. (IPENG),

**Dr. Adel El-Shahat, Senior IEEE Member**

Georgia Southern University, Statesboro, Georgia, USA

Assistant Professor, Department of Electrical Engineering,

Chapter (9): Breast Cancer Detection by Means of Artificial Neural Networks

a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One

country). Surely, this cancer will disappear from the world sooner than later.

Chapter (10): Applications of Artificial Neural Networks in Biofuels Chapter (11): ANN Modeling to Optimize Manufacturing Process

Chapter (13): Solar Radiation Prediction Using NARX Model

Forecasts or Combine Models?

Using Artificial Neural Networks

Chapter (8): Advanced Process Control

works

VIII Preface

Circulation Model

Best Regards, Adel

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73530

1. Introduction

Artificial neural network (ANN) is a computational structure inspired by a biological nervous system. An ANN consists of very simple and highly interconnected processors called neurons. The neurons are connected to each other by weighted links over which signals can pass. The process consists of data collection, analysis and processing, network structure design, number of hidden layers, number of hidden units, initializing, training the network, network simulation, weights/bias adjustments, and testing the network. Artificial neural networks are used in many different fields to process large sets of data, often providing useful analyses that allow for prediction and identification of new data. Artificial neural networks are computational structure programs consisting of interconnected processors called neurons connected by weights. They compute structural data through a process of learning and training. Data normally used by these structures have nonlinear relationships between inputs and outputs. They are used in applications such as speech recognition, imaging, control, estimation, optimization, and host of other things. They are also applied in real-world applications in the areas of finance, medical, business, mining, etc. [1].
