Neural Networks |
Artificial Neural Networks |
| Artificial Neural Networks What is ANN? Artificial neural networks are computational algorithms. |
What is ANN?ANN: Artificial Neural Networks (ANN) or neural networks are computational algorithms. It intended to simulate the behavior of biological systems composed of neurons. ANNs are computational models inspired by an animal's central nervous systems. It is capable of machine learning as well as pattern recognition. |
ANNArtificial Neural Networks - It intended to simulate the behavior of biological systems composed of "neurons". ANNs are computational models inspired by an animal’s central nervous systems. It is capable of machine learning as well as pattern recognition. These presented as systems of interconnected neurons which can compute values from inputs. A neural network is an oriented graph. It consists of nodes which in the biological analogy represent neurons, connected by arcs. It corresponds to dendrites and synapses. Each arc associated with a weight while at each node. Apply the values received as input by the node and define Activation function along the incoming arcs, adjusted by the weights of the arcs. |
Neural NetworksA neural network is a machine learning algorithm based on the model of a human neuron. The human brain consists of millions of neurons. It sends and process signals in the form of electrical and chemical signals. These neurons are connected with a special structure known as synapses. Synapses allow neurons to pass signals. From large numbers of simulated neurons neural networks forms. |
| Model of a neuron : dendrite (input units), synapse (connection weights), soma (summing function and computation), axon |
| Artificial Neural Networks (ANNs)
ANNs learn relationship between cause and effect or organize large volumes of data into orderly and informative patterns. A Neural Network is a biologically inspired information processing idea, modeled after our brain. A neural network is a large number of highly interconnected processing elements (neurons) working together Like people, they learn from experience (by example) Neural networks take their inspiration from neurobiology |
| A biological neuron has three types of main components; dendrites, soma (or cell body) and axon.
Dendrites receives signals from other neurons The soma, sums the incoming signals. When sufficient input is received, the cell fires; that is it transmit a signal over its axon to other cells. |
| An artificial neural network (ANN) is an information processing system that has certain performance characteristics in common with biological nets.
Several key features of the processing elements of ANN are suggested by the properties of biological neurons: |
| Artificial Neural Networks (ANNs) functions:
The processing element receives many signals. Signals may be modified by a weight at the receiving synapse. The processing element sums the weighted inputs. Under appropriate circumstances (sufficient input), the neuron transmits a single output. The output from a particular neuron may go to many other neurons. |
| From experience: examples / training data. Strength of connection between the neurons is stored as a weight-value for the specific connection. Learning the solution to a problem = changing the connection weights. |
| ANNs have been developed as generalizations of mathematical models of neural biology, based on the assumptions that:
Information processing occurs at many simple elements called neurons. Signals are passed between neurons over connection links. Each connection link has an associated weight, which, in typical neural net, multiplies the signal transmitted. Each neuron applies an activation function to its net input to determine its output signal. |
| A neural net consists of a large number of simple processing elements called neurons, units, cells or nodes.
Each neuron is connected to other neurons by means of directed communication links, each with associated weight. The weight represent information being used by the net to solve a problem. Each neuron has an internal state, called its activation or activity level, which is a function of the inputs it has received. Typically, a neuron sends its activation as a signal to several other neurons. It is important to note that a neuron can send only one signal at a time, although that signal is broadcast to several other neurons. |
| Neural networks are configured for a specific application, such as pattern recognition or data classification, through a learning process In a biological system, learning involves adjustments to the synaptic connections between neurons This is the same for artificial neural networks (ANNs) |
| A neuron receives input, determines the strength or the weight of the input, calculates the total weighted input, and compares the total weighted with a value (threshold)
The value is in the range of 0 and 1 If the total weighted input greater than or equal the threshold value, the neuron will produce the output, and if the total weighted input less than the threshold value, no output will be produced |
| What is Artificial Neural Networks? |