**Prerequisite :** Introduction to Artificial Neural Network

This article provides the outline for understanding the Artificial Neural Network.

**Characteristics of Artificial Neural Network**

- It is neurally implemented mathematical model
- It contains huge number of interconnected processing elements called neurons to do all operations
- Information stored in the neurons are basically the weighted linkage of neurons
- The input signals arrive at the processing elements through connections and connecting weights.
- It has the ability to learn , recall and generalize from the given data by suitable assignment and adjustment of weights.
- The collective behavior of the neurons describes its computational power, and no single neuron carries specific information .
- Implementation of Artificial Neural Network for AND Logic Gate with 2-bit Binary Input
- Implementation of Artificial Neural Network for OR Logic Gate with 2-bit Binary Input
- Implementation of Artificial Neural Network for XOR Logic Gate with 2-bit Binary Input
- Implementation of Artificial Neural Network for NOR Logic Gate with 2-bit Binary Input
- Implementation of Artificial Neural Network for XNOR Logic Gate with 2-bit Binary Input
- Implementation of Artificial Neural Network for NAND Logic Gate with 2-bit Binary Input
- Introduction to ANN (Artificial Neural Networks) | Set 3 (Hybrid Systems)
- Introduction to Recurrent Neural Network
- Neural Network Advances
- ANN - Self Organizing Neural Network (SONN)
- Importance of Convolutional Neural Network | ML
- Deep Neural Network With L - Layers
- ML - Neural Network Implementation in C++ From Scratch
- Implementation of neural network from scratch using NumPy
- A single neuron neural network in Python
- Difference between Neural Network And Fuzzy Logic
- ANN - Self Organizing Neural Network (SONN) Learning Algorithm
- ANN - Implementation of Self Organizing Neural Network (SONN) from Scratch
- Applying Convolutional Neural Network on mnist dataset
- Choose optimal number of epochs to train a neural network in Keras

**How simple neuron works ?**

Let there are two neurons **X** and **Y** which is transmitting signal to another neuron **Z** . Then , **X** and **Y** are input neurons for transmitting signals and **Z** is output neuron for receiving signal . The input neurons are connected to the output neuron , over a interconnection links ( **A** and **B** ) as shown in figure .

For above neuron architecture , the net input has to be calculated in the way .

**I = xA + yB**

where x and y are the activations of the input neurons X and Y . The output z of the output neuron Z can be obtained by applying activations over the net input .

**O = f(I)
Output = Function ( net input calculated )**

The function to be applied over the net input is called

*activation function*. There are various activation function possible for this.

** Application of Neural Network**

**1.** Every new technology need assistance from previous one i.e. data from previous ones and these data are analyzed so that every pros and cons should be studied correctly . All of these things are possible only through the help of neural network.

**2.** Neural network is suitable for the research on *Animal behavior, predator/prey relationships and population cycles* .

**3.** It would be easier to do *proper valuation* of property, buildings, automobiles, machinery etc. with the help of neural network.

**4.** Neural Network can be used in betting on horse races, sporting events and most importantly in stock market .

**5.** It can be used to predict the correct judgement for any crime by using a large data of crime details as input and the resulting sentences as output.

**6.** By analyzing data and determining which of the data has any fault ( files diverging from peers ) called as *Data mining, cleaning and validation* can be achieved through neural network.

**7.** Neural Network can be used to predict targets with the help of echo patterns we get from sonar, radar, seismic and magnetic instruments .

**8.** It can be used efficiently in *Employee hiring* so that any company can hire right employee depending upon the skills the employee has and what should be it’s productivity in future .

**9.** It has a large application in *Medical Research* .

**10.** It can be used to for *Fraud Detection* regarding credit cards , insurance or taxes by analyzing the past records .

**References :**

Wiki

doc journal

Principle of Soft Computing

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