data1_final.anns

xvxzczx

10395229_神经网络、模糊系统及其在运动控制中的应用_p239.pdf

人工神经网络（Artificial Neural Networks，简写为ANNs）也简称为神经网络（NNs）或称作连接模型（Connection Model），它是一种模仿动物神经网络行为特征，进行分布式并行信息处理的算法数学模型。这种网络依靠系统的复杂程度，通过调整内部大量节点之间相互连接的关系，从而达到处理信息的目的。 人工神经网络：是一种应用类似于大脑神经突触联接的结构进行信息处理的数学模型。在工程与学术界也常直接简称为“神经网络”或类神经网络。

twrds_unbiased_anns-源码

从有偏数据走向无偏神经网络 该存储库包含上述标题在我的硕士学位论文的实现中使用的所有代码。

Artificial Neural Networks_ New Research.pdf

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.

ANNS算法论文.zip

论文，关于ANNS算法的描述，包含算法NSG以及NSSG，包括相似最近邻算法的发展历程，包括理论论证，实现，分析，感兴趣的可以下载

Artificial Neural Networks_New Research-Nova Science(2017).pdf

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 variabl

Practical Computer Vision App Using D L with CNNs: With Detailed Examples in Py

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model. After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads. This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. What You Will Learn • Understand how ANNs and CNNs work • Create computer vision applications and CNNs from scratch using Python • Follow a deep learning project from conception to production using TensorFlow • Use NumPy with Kivy to build cross-platform data science applications Who This Book Is For Data scientists, machine learning and deep learning engineers, software developers.

Practical Computer Vision Applications Using Deep Learning with CNNs

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model. After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads. This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. What You Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using Python Follow a deep learning project from conception to production using TensorFlow Use NumPy with Kivy to build cross-platform data science applications

Artificial Neural Network Modelling 【2016】

"Artificial Neural Network Modelling" English | ISBN: 3319284932 | 2016 | 482 pages | PDF | 14 MB This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Beginning Artificial Intelligence with the Raspberry Pi-Apress(2017).pdf

Artificial intelligence, or AI, is an exciting field and my purpose in writing this book is to convey some of that excitement to you. I will be using the Raspberry Pi single-board computer as the primary tool through which you can explore how AI works and, consequently, gain additional insight on how you might incorporate AI into your projects and/or applications. I do want to make something perfectly clear at the outset: reading this book and completing all the projects will not make you an expert in AI. This is analogous to the situation where a layperson taking a first aid course could never claim to be a medical doctor or a nurse after taking that course. Becoming an AI expert requires that you take many college courses—both undergraduate and graduate—in a variety of areas, including mathematics, computer science, logic, and even philosophy. There are also AI experts who come from other spheres of interest, including music and the allied arts. Having made the previous statements, I do want you to understand that gaining a reasonable introduction to AI is very achievable by reading this book and other readily available resources. It is just that you should not try to claim that you are an AI expert after reading this book. I will next discuss why the Raspberry Pi is a good platform with which to examine AI. You should first note that it is a very capable computer on its own merit. Why certainly not as fast nor as memory capable as a modern PC or Mac, it is no slouch, especially when using a Raspberry Pi 3. This model has a clock speed of 1 GHz, uses four cores, and has 1 GB of dynamic ram. This is quite impressive when you realize that this performance comes with a price tag of only $35 (USD). However, the key feature that makes the Raspberry Pi so attractive for AI demonstrations is that it is a microcontroller. This means you can directly control things based upon the outcome of AI events. Microcontrollers also allow sensors to be easily connected to them, thus allowing AI applications a means to interact with the real world. While PCs can also be set up to both sense and control, it often requires expensive and complex interfaces to achieve these capabilities. The Raspberry Pi was initially designed to be able to sense and control devices with minimal interface requirements, and perhaps more importantly, minimal software requirements. PC software interfaces are often very complex, expensive, and typically proprietary—meaning making user changes or modifications is a difficult-to-impossible task. The Raspberry Pis that I use in this book use a Raspian Linux distribution named Jessie. This distribution is completely open source and freely available from the Raspberry Pi Foundation’s download website. It is a very stable operating system (OS) and supports several extremely large open-source applications repositories. This means that all the software used in this book is freely available and easily downloadable into the Raspberry Pi. xix ■ PrefaCe I use a variety of languages and applications in the book’s various demonstrations and projects. The languages used are mainly Python, Prolog, and the Wolfram Language. Each of these languages brings some unique features that allow the book demonstrations to be quickly and easily implemented. The main application that I use is Mathematica, which is a full-featured symbolic processing program that also happens to be part of the Jessie distribution. Mathematica is also a commercial program that ordinarily costs hundreds of dollars, but is provided gratis due to the very generous gift of the Wolfram Corporation and Dr. Stephen Wolfram (CEO) in particular. I tried to layout the book in a logical manner by first introducing AI in Chapter 1. It is difficult to explain AI to people who have never heard of it, although it is often surprising to inform that them that AI often affects them in their daily lives. I have provided a considerable amount of detail in the first chapter by trying to define AI and how it is commonly applied in everyday life situations. It will soon become apparent to you how invasive AI has become in modern society, whether you like it or not. Please note that I used the term invasive in a non-derogatory way, simply to point out that AI is commonly applied in many areas, some of which will surprise you. In addition, I also discuss the topic of business intelligence (BI), as it is very closely allied to AI and is often the vehicle through which AI affects most people. Some AI practitioners often refer to BI as simply AI applied in a business setting. You will learn that it is much more than that, however. I adopt it because it is a useful simplification. I next explore some basic AI concepts in Chapter 2. There is initially some discussion regarding basic logical constructs, as they are important to understand inference, which is an AI core foundation. Expert knowledge systems are next discussed, which constitute a major portion of the more general knowledge management systems (KMS)—an important part of BI. The discussion then turns to machine learning, which is a huge research area in modern AI. Finally, I conclude the chapter with an introduction to fuzzy logic (FL), which is thoroughly demonstrated in a later book project. Chapter 3 shows you how to implement a practical expert system using the Prolog language. I explore some key Prolog features and explain how this somewhat specialized language is so useful in implementing AI concepts, without requiring extensive programing support as would be necessary if general-purpose languages such as C/C++ or Java were used for the same purposes. A simple console question-and-answer program is used in the practical demonstration. Chapter 4 focuses on using AI with games. Admittedly, the games are quite simple; however, the chapter’s goal is to simply demonstrate how AI is incorporated into gaming logic. These gaming AI concepts may then be easily expanded to handle much more complex games. I used Python to implement the games, which are controlled through a traditional text-console interface. Do not expect to see World of Warcraft (WoW)–quality graphics in this chapter, but rest assured that WoW does use AI in its games. In Chapter 5, I return to using Prolog to implement some fuzzy logic controls for a practical project demonstration. There is also a simplified expert rules system incorporated into the project. A Raspberry Pi system using both temperature and humidity sensors will control a virtual heating and cooling system. Chapter 6 introduces the concept of shallow machine learning. A Python program is created, in which the computer “learns” your favorite color and make “decisions” regarding color selection. Finally, I close the chapter with a discussion of adaptive learning, which plays a large role in BI. xx Chapter 7 continues the machine learning topic with an examination of machine learning using artificial neural networks (ANN). ANNs are by far the most prevalent AI method used to implement machine learning. I go through a detailed discussion on how an ANN is constructed, and then demonstrate an actual neural network created with Python. The machine learning continues into Chapter 8, where deep learning is discussed. In this chapter’s project I go through a detailed discussion on how a multi-layer ANN functions incuding the gradient search feature. Chapter 9 contains two demonstrations of deep learning using multi-layer ANNs. The first one recognizes hand-wriiten numbers based on the MNIST training and test dataset. The second one uses a Pi Camera with a Raspberry Pi to image a hand-written number and then uses the previously trained ANN to determine the closest match. Chapter 10 deals with evolutionary computing (EC), which encompasses, but is not limited to, evolutionary programming, genetic algorithms and genetic programming. I have provided several interesting demonstrations highlighting some of the EC features to provide you with a good introduction to this fascinating field. Chapter 11 discusses subsumption, which is a behavior-based robotic study area. It is closely allied with AI. I use the robot car first introduced in Chapter 7 to conduct several demonstrations. You will quickly realize that a robot employing subsumption behaviors can remarkably mimic actual human behavior, thus completing the AI loop between human thinking and motor behavior. I am quite confident that after reading through this book and duplicating most—if not all the projects and demonstrations, you will come away with an excellent appreciation of AI and how to incorporate it into your future projects.

swarm intelligence is a interesting threory in ANNs,

swarm intelligence 是一个很好的关键在计算智能中

Machine Learning for Wireless Networks

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

论文研究-基于小波重构的控制图并发异常模式识别研究.pdf

对于统计质量控制过程中的复杂过程而言，多种异常的并发现象比较普遍，而常规的基于规则的方法以及人工神经网络（ANNs）技术均针对单一异常模式的识别，难以完成对并发异常模式的识别任务。提出一种混合方法，将小波分析与ANNs相结合，通过小波分解重构将并发异常模式分解为基本的异常模式组合，无须用并发异常样本训练ANNs，实现对并发异常模式的有效识别。