Artificial Neural Networks: The Future of Intelligent Computing
- December 23, 2021
- Posted by: FIZ Robotic Solutions
- Category: Artificial Intelligence
According to Alan Turing – “What we want is a machine that can learn from experience.”
Have you ever wondered how artificial intelligence-based robots and systems function? Artificial neural networks (ANN), which are the lifeblood of AI-powered systems, are the answer. Warren McCulloch and Walter Pitts created a computational model for neural networks based on mathematics and algorithms called threshold logic in 1943. This model established the groundwork for future neural network research. The biological neural network of the human brain inspired artificial neural networks. The human brain is made up of neurons, or nerve cells, which transmit and process information from the senses. A network of nerves is formed in our brain by the arrangement of many nerve cells.
Similarly, in artificial neural networks, there are artificial neurons that perform all functions similar to those of neurons in the human brain. Each artificial neuron transmits information to the other neuron in the same way that biological neurons do in the human brain. The “edge” connection between artificial neurons. Artificial neurons are organised into layers. These various layers are interconnected and used to complete the desired task. Artificial Neural Networks are made up of three layers: input layers, output layers, and hidden layers between the input and output layers.
In artificial neural networks, the hidden layer is a layer of neurons whose output is connected to the inputs of other neurons and thus is not visible as a network output. The information from the features is used by each hidden layer in the neural network. By discovering relationships between features in the input, hidden layers capture more and more complex information with each layer. Let us discuss it in mathematical terms to better understand it.
Mathematical Model of Artificial Neural Network-
Weighted directed graphs are used in Artificial Neural Networks, where artificial neurons are called nodes and directed edges with weights connect neuron outputs and neuron inputs. The Artificial Neural Network collects information from the outside world in the form of patterns and vector images. The notation x is used to represent inputs in mathematics (n).
Where n= number of inputs
Each input is multiplied by the weights assigned to it. The neural network uses weights to solve a problem. Weight represents the strength of the neural network’s interconnection between neurons. The weighted inputs are totaled within the computing unit (artificial neuron). If the weighted sum is zero, bias is applied to the output to make it non-zero or to scale up the system response. The weight and input of Bias are always equal to ‘1′.
The sum can be any numerical value between 0 and infinity. The threshold value is set to limit the response to the desired value. The sum is then passed through an activation function. The activation function is set to the desired output transfer function. There are both linear and nonlinear activation functions.
Don’t be bored by the mathematical model; we have something fun and interesting to explain how neural networks work at a lower level. Let’s break it down into simpler terms by looking at the diagram below-
This diagram depicts how an artificial neural network learns through experience. Artificial neural networks (ANN) can learn by adjusting the strength of their connections to improve the transfer of input signals through multiple layers of neurons associated with general concepts.
As shown in the figure, a neural network works one by one in different layers, which explains the internal workings of neural networks. That is how artificial neural networks work at various levels of image recognition.
Types of Artificial Neural Networks-
Artificial Neural Networks (ANN) are classified into several types based on their features and various functions. However, we will focus on the most fundamental neural networks, which serve as the foundation for more advanced neural networks. They are as follows:
I. Feedforward Artificial neural network-
The feedforward neural network was the first and most basic type of artificial neural network. All nodes in a layer are connected to nodes in previous layers in different layers. Different weights are assigned to the connection. Because there is no feedback loop, the signal can only flow one way, from input to output.
II. Feedback Artificial Neural Network-
A feedback network contains feedback paths, which allow the signal to flow in both directions via loops. Feedback networks are more efficient than feedforward networks because they use feedback to achieve the best results.
III. Convolutional Neural Network-
Convolutional neural networks (CNNs) are a type of deep neural network that employs multilayer perceptrons. Multilayer perceptrons are fully connected networks in which each neuron in one layer is linked to all neurons in the next layer. CNNs are used to perform image processing, natural language processing, and other cognitive tasks.
Artificial Neural Networks Applications-
Artificial Neural Networks due to their vast utility in every sector have extensive applications used to perform various task. –
I. Speech Recognition-
As voice technology advances, ANNs are being used in applications such as automated telephone conversations, home automation, mobile telephony, virtual assistance, hands-free computing, video games, and so on. In this field, neural networks are widely used. Neural networks can be specifically programmed to handle a wide variety of queries, and with continuous learning, neural networks can help you achieve great speech recognition software.
II. Character Recognition-
Artificial neural networks are used to solve real-world problems quickly and efficiently. ANNs are making a name for themselves in the field of character recognition. Character recognition includes handwriting recognition, which is useful in reducing fraud in banks and at all levels. Similarly, there are many applications in image recognition, such as facial recognition in social media platforms, as seen on Facebook – when you upload any photo, the service automatically highlights faces and prompts friends to tag this is due to ANNs.
Forecasting the behaviour of things has become increasingly important in recent years. Forecasting aids in sales decisions, economic and monetary policy, finance, and the stock market. In addition, in weather forecasting, neural networks are involved in the real-time processing of satellite and radar images, which not only detect the early formation of hurricanes and cyclones, but also detect sudden changes in wind speed and direction, which indicate any impending disasters.