# Artificial Neural Networks

ANN learning is robust to errors in the training data and has been successfully applied for learning real-valued, discrete-valued, and vector-valued functions containing problems such as interpreting visual scenes, speech recognition, and learning robot control strategies. The study of artificial neural networks (ANNs) has been partly inspired by the observation that biological learning systems are built of very complex webs of interconnected neurons in brains. The human brain contains a densely interconnected network of approximately 10^11-10^12 neurons, each connected neuron, on average connected, to l0^4-10^5 other neurons. So on average human brain takes approximately 10^-1 to make surprisingly complex decisions. ANN systems are motivated to capture this highly parallel computation based on distributed representations.

Difference between Biological Neurons and Artificial Neurons

Characteristics of Artificial Neural Network

• It is a neutrally implemented mathematical model
• It contains a huge number of interconnected processing elements called neurons to do all operations
• Information stored in the neurons is 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.

How simple neuron works?

• Let there be two neurons X and Y which transmit a signal to another neuron Z. Then, X and Y are input neurons for transmitting signals and Z is output neurons for receiving call. The input neurons are connected to the output neuron, over an 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 the 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 judgment 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 the right employee depending upon the skills the employee has and what should be its 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

Advantage of Using Artificial Neural Networks:

• Problem in ANNs can have instances that are represented by many attribute-value pairs.
• ANNs used for problems having the target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes.
• ANN learning methods are quite robust to noise in the training data. The training examples may contain errors, which do not affect the final output.
• It is used generally used where the fast evaluation of the learned target function may be required.
• ANNs can bear long training times depending on factors such as the number of weights in the network, the number of training examples considered, and the settings of various learning algorithm parameters.