Found 27 Articles for Neural Network

In an electrical system, there are three types of conductors used, they are phase, neutral, and ground. In general, the neutral wire is grounded for safety and stability reasons. But sometimes, a condition occurs where the neutral wire of the systems is disconnected from the ground terminal, this condition is known as floating neutral. In some special situations, the neutral wire is intentionally left isolated from the ground terminal, this is also known as floating neutral. In this article, we will learn about floating neutral in electrical systems, its effects, causes, and methods of testing and fixing the floating ... Read More

In the field of electrical and power distribution engineering, there are two important concepts namely neutral and floating neutral. The neutral, also called neutral wire, is a return path for electric current in electrical circuits that balances the phase loads and serves as a reference point. In practical applications, the neutral is typically grounded at the main panel or distribution end. On the other hand, the floating neutral is also a neutral wire but it is isolated from the ground terminal. The concept of floating neutral is not much common in practical applications, but it may be used in ... Read More

Introduction Have you ever wondered how humans are able to perceive and understand the visual world with limited sensory inputs? It's a remarkable ability that allows us to compose complex visual concepts from basic elements. In the field of computer vision, scientists have been trying to mimic this compositional behavior using convolutional neural networks (CNNs). CNNs use convolution layers to extract features from images, but they have limitations when it comes to modeling visual elements with varying spatial distributions. The Problem With Convolution Convolution layers in CNNs work like pattern matching processes. They apply fixed filters to spatially aggregate input ... Read More

John Hopfield came up with the Hopfield Neural Network in 1982. In 1982, John Hopfield developed what is now known as the Hopfield Neural Network. It's a synthetic network that mimics the brain's activity. This recurrent neural network can model associative memory and pattern recognition issues. The Hopfield Neural Network helps find solutions to various issues. Image and voice recognition, optimization, and combinatorial optimization are just some of the numerous applications that have benefited from their use. The Architecture of the Hopfield Neural Network A Hopfield Neural Network mainly consists of a single layer of interconnected neurons. An ultimately linked ... Read More

DPCCNN, or "Deep parametric Continuous Convolutional Neural Network, " is a type of neural network that is used, among other things, to classify pictures, find objects in pictures, and divide up pictures into parts. DPCCNN is an upgraded version of Convolutional Neural Networks (CNNs) that use continuous functions instead of discrete convolutional filters. Parametric Continuous Convolution In DPCCNNs, convolution is done with a function called the parametric continuous convolution (PCC), which is a continuous function. Considered a function, PCC takes an image and some values as input, returns a continuous function as output, and gets a convolutional result. Architecture DPCCNNs ... Read More

Introduction The introduction briefly overviews artificial neural networks and the Adaline architecture. It explains the concept of an OR gate, a fundamental logic gate used in digital circuit design. The goal is to train the Adaline network to output the correct OR gate truth table given different input combinations. Define the Input and Output Identify the input and output patterns for the OR gate. In the case of the OR gate, there are two input variables (x1 and x2) and one output variable (y). Generate Training Data Create a set of input-output training patterns that cover all possible combinations of ... Read More

ANN or artificial neural networks are the computing systems developed by taking inspiration from the biological neural networks; the human brain being its major unit. These neural networks are made functional with the help of training that follows some kind of a learning rule. A learning rule in ANN is nothing but a set of instructions or a mathematical formula that helps in reinforcing a pattern, thus improving the efficiency of a neural network. There are 6 such learning rules that are widely used by neural networks for training. Hebbian Learning Rule Developed by Donald Hebb in 1949, ... Read More

A transfer neural network is a deep learning architecture that handles long-range dependencies well, as was first described in Vaswani et al's 2017 paper "All you need is attention.". The self-attention mechanism of transformer networks allows them to identify relevant parts of input sequences. What are Recurrent Neural Networks? Recurrent neural networks are artificial neural networks that have memory or feedback loops. They are designed to process and classify sequential data in which the order of the data points is important. The network works by feeding the input data into a hidden layer, allowing the network to maintain information ... Read More

TFlearn is an open-source deep-learning library built on the TensorFlow framework. It provides a high-level API with which it is easy to create and train different neural network models. It provides an array of pre-existing models such as Convolutional Neural Network (CNN), Deep Neural Networks (DNN), and many other models. It also includes a variety of activation functions such as ReLU (Rectified Linear Units), softmax, and also loss functions such as categorical cross-entropy and so on. TfLearn is an ideal library for beginners due to not requiring extensive knowledge of Neural Network APIs in TensorFlow. It is a simple, easy-to-use ... Read More

Neural Network is a widely used concept in the field of Artificial Intelligence and is based on the structure of the human brain. A neural network works in layers, the simplest one being a sequential model where the input of the current layer is the output of the previous layer. To create, train and test a neural network model, we can use a deep learning framework like Tensorflow in Python. Every neural network model is based on a few simple steps like taking data, making predictions, comparing predictions and finally, changing them to go closer to the target. ... Read More