artificial neural network
TRANSCRIPT
كلية العلوم و Informationاآلداب بتثليث
System
By: Wasmeiah Theeb
Aiyd Monerah Faleh Shekhah Masoud Wasmeiah Theeb
Hezam Sarah Nasser Rajsa Nasser
:Guided byMohammed Sheraz
ARTIFICIAL NEURAL NETWORKS
CONTENTS
INTRODUCTIONBIOLOGICAL NEURON MODELARTIFICIAL NEURON MODELARTIFICIAL NEURAL NETWORKNEURAL NETWORK ARCHITECTURESINGLE-LAYER FEED FORWARD NETWORKMULTILAYER FEED FORWARD NETWORKSLEARNING IN ANNAPPLICATIONSADVANTAGESDISADVANTAGES
INTRODUCTION
“Neural“ is an adjective for neuron, and “network” denotes a graph like structure.
Artificial Neural Networks are also referred to as “neural nets” , “artificial neural systems”, “parallel distributed processing systems”, “connectionist systems”.
Neural networks are the simplified models of the biological neuron systems.
Neural networks are typically organized in layers. Layers are made up of a number of interconnected 'nodes' .which contain an 'activation function'.
BIOLOGICAL NEURON MODELFour parts of a typical
nerve cell : - DENDRITES: Accepts the
inputs SOMA : Process the
inputs AXON : Turns the
processed inputs into outputs.
SYNAPSES : The electrochemical contact between the neurons.
ARTIFICIAL NEURON MODEL
•Inputs to the network are represented by the mathematical symbol, xn
•Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
•These products are simply summed, fed through the transfer function, f( ) to generate a result and then output.
f
w1
w2
xn
x2
x1
wn
f(w1 x1 + ……+ wnxn)
ARTIFICIAL NEURAL NETWORK
Artificial Neural Network (ANNs) are programs designed to solve any problem by trying to mimic the structure and the function of our nervous system.
Neural networks are based on simulated neurons, Which are joined together in a variety of ways to form networks.
Neural network resembles the human brain in the following two ways: -* A neural network acquires knowledge through learning. *A neural network’s knowledge is stored within the interconnection strengths known as synaptic weight.
Inputs
Output
ARTIFICIAL NEURAL NETWORK
An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.
NEURAL NETWORK ARCHITECTURES
The neural network in which every node is connected to every other nodes, and these connections may be either excitatory (positive weights), inhibitory (negative weights), or irrelevant (almost zero weights).
Input node
Input node
output node
output node
Hidden node
Fig: fully connected network
These are networks in which nodes are
partitioned into subsets called layers, with no
connections from layer j to k if j > k .
Layer 1 Layer2 Layer0 Layer3
(Input layer) (Output layer)
Hidden Layer
fig: layered network
NEURAL NETWORK ARCHITECTURES
This is the subclass of the layered networks in which there is no intra-layer connections. In other words, a connection may exist between any node in layer i and any node in layer j for i < j, but a connection is not allowed for i=j.
Fig : Acyclic network
Layer 1 Layer2 Layer0 Layer3
(Input layer) (Output layer)
Hidden Layer
NEURAL NETWORK ARCHITECTURES
Layer 1 Layer2 Layer0 Layer3
(Input layer) (Output layer)
Hidden Layer
This is a subclass of acyclic networks in which a connection is allowed from a node in layer i only to nodes in layer i+1
fig : Feedforward network
NEURAL NETWORK ARCHITECTURES
Many problems are best solved using neural networks whose architecture consists of several modules, with sparse interconnections between them. Modules can be organized in several different ways as Hierarchical organization, Successive refinement, Input modularity
Neural network architecture
An artificial Neural Network is defined as a data processing system consisting of a large number of interconnected processing elements or artificial neurons. There are three fundamentally different classes of neural networks. Those are. 1. Single layer feedforward Networks. 2. Multilayer feedforward Networks. 3. Recurrent Networks.
Single-layer Feed Forward Network
Single-layer Feedforward Networks
This kind of networking contain input layer and output layer and one layer of calculations
Multilayer Feedforward Networks
Multilayer Feedforward Networks
This type of network contains several layers and separated from the first type is the hidden layers that areProcessing
Learning in ANN
Learning methods in neural networks can be broadly classified in three basic types. - Supervised Learning - Unsupervised Learning - Reinforcement LearningSupervised Learning:- In supervised learning, both the inputs and the outputs are
provided. The network then processes the inputs and compares its resulting outputs against the desired outputs
Errors are then calculated, causing the system to adjust the weights which control the network.
Here a teacher is assume to be present during the learning process..
Neural networks are not programmed but it is education and there are many of them learning algorithms Back
Propagation algorithm (an algorithm based propagation of errors from back to front to set the network) weights and
method of Hebb Rule.
Learning in ANN
Unsupervised Learning:- Here the target output is not presented to the
network, Because there is no teacher to present the described patterns.
So the system learns of its own by discovering and adapting to structural features of the input patterns.
Reinforcement Learning:- In this method, a teacher though available, does
not present the expected answer but only indicates if the computed output is correct or incorrect.
The information provided helps the network in its learning process.
Here a reward is given for correct answer computed and a penalty for a wrong answer.
APPLICATIONS OF NEURAL NETWORKS
Character Recognition:- Neural networks can be used to recognize handwritten characters.
Image Compression:- Neural networks can receive and process vast amounts of information at once, making them useful in image compression.
Stock Market Prediction:- Neural networks can examine a lot of information quickly and sort it all out, they can be used to predict stock prices.
Travelling Salesman Problem:- Neural networks can solve the traveling salesman problem, but only to a certain degree of approximation.
Security and Loan Applications:- With the acceptation of a neural network that will decide whether or not to grant a loan.
1.Pattern recognition2.Investment analysis3.Control systems &4. monitoring5.Marketing and financial applications6.Mobile computing7.Forecasting – sales, market research, meteorology
APPLICATIONS OF NEURAL NETWORKS
: ADVANTAGES
A neural network can perform tasks that a linear program can not.When an element of the neural network fails, it can continue without any problem by their parallel nature. A neural network learns and does not need to be reprogrammed.It can be implemented in any application.It can be implemented without any problem
DISADVANTAGES
The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.
Requires high processing time for large neural networks The neural network needs training to operate.