In addition, the Neural Network has self–learning capabilities to produce better results as more data becomes available.
Artificial Neural Network (ANN) consists of four essential parts:
- Layers: The entire learning process takes place in layers. There are three layers;
- Input Layer
- Output Layer
- Hidden Layers
- Optimizer: The part that improves learning by updating information on the network,
- Feature and Tag: Data entry to the network (properties) and network exit (tags),
- Error Calculation Function (Loss Function): The metric used to predict the performance of the learning phase.
A Neural Network takes the input data and transmits it to the collection of layers. First, a Neural Network needs to evaluate its performance with a loss function. The Error Calculation Function gives the Neural Network an idea of the path it must follow before it can master the information. Then, the network needs to improve its knowledge with the help of an optimizer.
The program takes the input values and transmits them to two interconnected layers. Then, you apply what you know to solve the problem. The probability of success in the first stage is very low; The same is true for the Neural Network. When he first saw the data, his prediction may not match the expected results perfectly. Therefore, the grid uses an optimizer to improve its knowledge. With a simple analogy, you might think that the optimizer’s responsibility is to reread the relevant section. By rereading, you gain new insights and gains.
Similarly, using the Neural Network optimizer, it updates its knowledge and tests its new ability to check how much it needs to learn. The program repeats these steps until it makes the lowest possible error. Returning to our math problem analogy, you read the textbook chapter many times before you fully understand the course content. If you continue to make mistakes even after reading it many times, you have reached the knowledge capacity you can have with the existing material. You need to use a different textbook or test with another method to improve your score. The same is true for a Neural Network. If the error rate does not decrease, the Neural Network cannot learn anything else with this architecture. To improve knowledge, the network needs to be better optimized.
Advantages of Artificial Neural Networks
- They can learn with different learning algorithms.
- They can work in parallel and process real–time information.
- They can produce results (information) for unseen outputs. There is unsupervised learning.
- They can make pattern recognition and classification. They can complete the missing patterns.
- Artificial Neural Networks consist of many cells, and these cells work simultaneously to perform complex tasks.
- They have fault tolerance. They can work with incomplete or unclear information. In faulty conditions, they show graceful degradation.
- Artificial Neural Networks are mainly used in diagnosis, classification, prediction, control, data association, filtering, and interpretation. To determine which mesh is more suitable for which problem, it is necessary to compare the properties of the networks with the properties of the issues.
Usage Areas of Artificial Neural Networks
- Energy Production: Price and Load Forecasting
- Computational Finance: Credit Scoring, Algorithmic Trading
- Natural Language Processing: Voice Assistant, Emotion Analysis
- Automotive, Aerospace, and Manufacturing: Predictive Maintenance
- Computational Biology: Tumor Detection, Durg Discovery, DNA Sequencing
Biological Fundamentals of Artificial Neural Networks
Artificial Neural Networks consist of neurons (nerve cells). Neurons can process information. Neurons connect to form functions. A neuron can make between 50,000 and 250,000 connections with other neurons.
Cybernetics is examined by examining the behavior of living things, modeling them mathematically, and producing similar artificial models. The aim is to model the learning and application structure of the human brain with Neural Networks that can be trained, self–organized, learned, and evaluate. To perform a job on a computer, it is necessary to know its algorithm. The algorithm is the complete set of basic scripts for converting input to output. However, there may not be a known algorithm for solving some problems. Applications that may change over time in desired and undesired situations or vary according to the user do not have fixed algorithms. Even if our knowledge is lacking, our data can be plentiful. We can easily make the system learn from thousands of desirable and undesirable samples.
Since data collection devices are digital in today’s technology, the data can be accessed, stored, and processed reliably, which gives us an advantage.
Basic Components of Artificial Neural Networks
- Architectural Building
- Learning Algorithm
- Activation Function
Since these components directly affect the foresight performance, the one suitable for the structure of the data should be preferred at the decision point.
- Architectural Structure
A 3–layer (or layered) feedforward neural network model consists of Input, Hidden, and Output layers.
- Learning Algorithm
The ability to learn from an information source is one of the essential features of Artificial Neural Networks (ANN). In neural networks, information is held in the weights of the connections of the neurons in the network. Therefore, it is crucial how the consequences are determined. Since the data is stored in the entire network, the weight value of a node does not mean anything by itself. The weights in the whole network should take optimal values. The process to reach these weights is called “training the network.” Accordingly, the weight values must be dynamically changeable within a specific rule for a network to be trainable.
To put it briefly, We can define the learning process as finding the best value of the weights.
- Activation Function
The activation function provides curvilinear coupling between input and output units (layers). The correct selection of the activation function significantly affects the network’s performance. The activation function can generally be selected as unipolar (0 1), bipolar (–1 + 1) and linear. It is the network component that enables it to learn the nonlinear structure.
