intelligent water networks

Development of Computer Networks (Understanding) Remote Terminal Connection Stage: The early stage of computer networks, where terminals were connected to a central mainframe for data access. Computer-to-Computer Network Stage: This stage marked the beginning of direct communication between computers, laying the foundation for modern networking. Computer Network Interconnection Stage: The evolution of networking where multiple networks were interconnected, forming the internet. Information Superhighway Stage: The current phase, characterized by high-speed data transfer and advanced networking technologies, forming the backbone of global communications. Content Slides

回顾了PID控制器的发展历程，重点介绍了基于专家系统、模糊控制和神经网络的智能PID控制器的研究概况，并对今后的PID控制发展进行了展望。这些信息对我们理解PID控制技术及其改进具有重要帮助。

刘金琨《智能控制》书本的所有源程序，matlab编程，希望大家有用。

Normalization Issues In neural network training, normalization is crucial to ensure consistent model performance and faster convergence. Below are key normalization methods: Normalization Method One E and E’ Distance metric (d) adjustments Normalization is used to transform input data, enhancing the efficiency of the network by bringing diverse features into a common scale. This approach helps in minimizing gradient issues, ensuring stable and accelerated training progress.

The EPANET-Matlab Toolkit is an open-source software initially developed in Matlab, providing a programming interface for the latest version of hydraulic and quality modeling software developed jointly by the US EPA and Matlab. This toolkit serves as a general programming framework for research and development in the growing field of intelligent water networks, offering easy-to-use commands and wrappers for viewing, modifying, simulating, and plotting results generated by the EPANET library. For support and citation, refer to the paper by DG Eliades et al. at the 14th International Conference on Computing and Control for the Water Industry (CCWI), Netherlands, November 2016, page 8 (doi: 10.5281/zenodo.831493).

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。

脑血管系统是一个复杂的血管网络，为大脑提供重要的营养和氧气。这种系统易于遭受出血、感染、血栓等损伤，常常需要进行脑部手术。然而，手术时通常无法直接进入手术地点，因此必须寻找替代入口点和路径。提出的系统利用MRA图像上的图像处理和路径查找技术，帮助医生/外科医生找到脑血管系统中两点之间的最短距离。论文链接：ACM Paper

生成对抗神经网络MATLAB代码 生成对抗神经网络的MATLAB代码已被弃用。建议使用NeuralTalk2，它比旧版本快约100倍，支持在GPU上进行批处理和CNN微调。此项目包括使用Python和Numpy的源代码，通过多模态循环神经网络为图像生成描述。 项目概述 输入数据为使用Amazon Mechanical Turk收集的图像及其5个句子描述。训练阶段中，图像作为输入，RNN根据上下文预测句子中的单词，网络的参数在这一过程中不断更新。

listener.ora Configuration Guide The listener.ora file is an essential configuration file for Oracle databases. It specifies the settings for the database listener, enabling communication between the database and client applications. Below is an overview of its structure and common configuration parameters. Key Sections of listener.ora Listener Settings Defines listener names and network protocol addresses. Global Database Service Configuration Specifies global database names and associated service identifiers. Logging and Tracing Parameters Configures log files and trace levels for better debugging and tracking. Example Configuration of listener.ora LISTENER = (DESCRIPTION_LIST = (DESCRIPTION = (ADDRESS = (PROTOCOL = TCP)(HOST = myhost)(PORT = 1521)) ) ) SID_LIST_LISTENER = (SID_LIST = (SID_DESC = (GLOBAL_DBNAME = mydb.example.com) (ORACLE_HOME = /oracle/product/12.1.0) (SID_NAME = mydb) ) ) Important Tips Always back up listener.ora before making changes. Verify settings by using lsnrctl commands to start, stop, or check listener status. Use this guide as a reference to ensure your Oracle database connections remain stable and secure.

数据归一化将数据映射到[0,1]或[-1,1]或其他区间。为什么归一化？ 1. 输入数据单位不一样，有些数据的范围特别大，导致神经网络收敛慢，训练时间长。 2. 数据范围大的输入在模式分类中的作用可能偏大，范围小的作用可能偏小。 3. 由于输出层激活函数的值域有限制，需将数据映射到激活函数的值域。