Artificial Neural Network Tutorial | What is Artificial Neural Network
What is Artificial Neural Network?
Artificial Neural Network
- The term "Artificial Neural Network" is derived from Biological neural networks that develop the structure of a human brain.
- Similar to the human brain that has neurons interconnected to one another; Artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes.
Artificial Neural Network - Biological Neuron Network
Typical Artificial Neural Network
Artificial Neural Network Tutorial
- Dendrites from Biological Neural Network represent inputs in Artificial Neural Networks, cell nucleus represents Nodes, synapse represents Weights, and Axon represents Output.
Relationship between Biological neural network and artificial neural network
| Biological Neural Network | Artificial Neural Network |
|---|---|
| Dendrites | Inputs |
| Cell nucleus | Nodes. |
| Synapse. | Weights |
| Axon | Output . |
- An Artificial Neural Network in the field of Artificial intelligence where it attempts to mimic the network of neurons makes up a human brain so that computers will have an option to understand things and make decisions in a human-like manner.
- The ANN is designed by programming computers to behave simply like interconnected brain cells.
- There are around 1000 billion neurons in the human brain. Each neuron has an association point somewhere in the range of 1,000 and 100,000. The human brain is made up of incredibly amazing parallel processors.
- The artificial neural network with an example, consider an example of a digital logic gate that takes an input and gives an output. "OR" gate, which takes two inputs. If one or both the inputs are "On," then we get "On" in output. If both the inputs are "Off," then we get "Off" in output. Here the output depends upon input. Our brain does not perform the same task. The outputs to inputs relationship keep changing because of the neurons in our brain, which are "learning."
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Architecture of an Artificial Neural Network
- In order to define a neural network that consists of a large number of artificial neurons, which are termed units arranged in a sequence of layers. Artificial Neural Network primarily consists of three layers:
Architecture of Artificial Neural Network
- Input Layer:
- It accepts inputs in several different formats provided by the programmer.
- Hidden Layer:
- The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.
- Output Layer:
- The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer.
- The artificial neural network takes input and computes the weighted sum of the inputs and includes a bias. This computation is represented in the form of a transfer function.
- It determines weighted total is passed as an input to an activation function to produce the output. Activation functions choose whether a node should fire or not. Only those who are fired make it to the output layer. There are distinctive activation functions available that can be applied upon the sort of task we are performing.
Transfer Function
Advantages of Artificial Neural Network (ANN)
Advantges of Artificial Neural Network
Parallel processing capability
- Artificial neural networks have a numerical value that can perform more than one task simultaneously.
Storing Data on the Entire Network
- Data that is used in traditional programming is stored on the whole network, not on a database.
Capability to work with incomplete knowledge
- After ANN training, the information may produce output even with inadequate data. The loss of performance here relies upon the significance of missing data.
Having a memory distribution
- The succession of the network is directly proportional to the chosen instances, and if the event can't appear to the network in all its aspects, it can produce false output.
Having fault tolerance
- Extortion of one or more cells of ANN does not prohibit it from generating output, and this feature makes the network fault-tolerance.
Disadvantages of Artificial Neural Network
Disadvantages of Artificial Neural Network
Assurance of proper network structure
- There is no particular guideline for determining the structure of artificial neural networks. The appropriate network structure is accomplished through experience, trial, and error.
Unrecognized behavior of the network
- It is the most significant issue of ANN. When ANN produces a testing solution, it does not provide insight concerning why and how. It decreases trust in the network.
Hardware Dependence
- Artificial neural networks need processors with parallel processing power, as per their structure. Therefore, the realization of the equipment is dependent.
Difficulty of showing the issue to the Network
- ANNs can work with numerical data. Problems must be converted into numerical values before being introduced to ANN. The presentation mechanism to be resolved here will directly impact the performance of the network. It relies on the user's abilities.
The duration of the network is unknown
- The network is reduced to a specific value of the error, and this value does not give us optimum results.
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How do artificial neural networks work ?
- The association between the neurons outputs and neuron inputs can be viewed as the directed edges with weights.
- The Artificial Neural Network receives the input signal from the external source in the form of a pattern and image in the form of a vector.
- These inputs are then mathematically assigned by the notations x(n) for every n number of inputs.
- In general terms, these weights normally represent the strength of the interconnection between neurons inside the artificial neural network. All the weighted inputs are summarized inside the computing unit.
- If the weighted sum is equal to zero, then bias is added to make the output non-zero or something else to scale up to the system's response. Bias has the same input, and weight equals to 1.
- The total of weighted inputs can be in the range of 0 to positive infinity. Here, to keep the response in the limits of the desired value, a certain maximum value is benchmarked, and the total of weighted inputs is passed through the activation function.
- The activation function refers to the set of transfer functions used to achieve the desired output. There is a different kind of the activation function, but primarily either linear or non-linear sets of functions.
Some of the commonly used sets of activation functions are the Binary, linear, and Tan hyperbolic sigmoidal activation functions. Let us take a look at each of them in details:
Binary
- In binary activation function, the output is either a one or a 0. Here, to accomplish this, there is a threshold value set up. If the net weighted input of neurons is more than 1, then the final output of the activation function is returned as one or else the output is returned as 0.
Sigmoidal Hyperbolic
- The Sigmoidal Hyperbola function is generally seen as an "S" shaped curve. Here the tan hyperbolic function is used to approximate output from the actual net input. The function is defined as:
F(x) = (1/1 + exp(-????x)) - Where ???? is considered the Steepness parameter.
Types of Artificial Neural Network:
Types of Artificial Neural Network
- There are various types of Artificial Neural Networks (ANN) depending upon the human brain neuron and network functions, an artificial neural network similarly performs tasks.
- The majority of the artificial neural networks will have some similarities with a more complex biological partner and are very effective at their expected tasks
Feedback ANN
- The output returns into the network to accomplish the best-evolved results internally.
- The feedback networks feed information back into itself and are well suited to solve optimization issues. The Internal system error corrections utilize feedback ANNs.
Feed-Forward ANN:
- A feed-forward network is a basic neural network comprising of an input layer, an output layer, and at least one layer of a neuron.
- Through assessment of its output by reviewing its input, the intensity of the network can be noticed based on group behavior of the associated neurons, and the output is decided.
- The primary advantage of this network is that it figures out how to evaluate and recognize input patterns.