Artificial neural network
- 1. Nadar Sarawathi College Of Arts & Science, Theni. R.Preethi. S.Subhalakshmi.
- 2. Introduction: Artificial Neural Networks are computational models inspired by human brain, used to solve complex problems. This paper is written to introduce artificial neural networks with new comers from computers science researchers and developers. This paper covers only those concepts from Biological Neural Network which are compulsory for computer science field.BNN have many other parts which are not covered here because of unnecessity.To understand ANN, basics of BNN(nervous system) should be clear.
- 3. Artificial Neural Network. The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites. The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons.
- 4. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward. ANNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data.
- 5. The result of these operations is passed to other neurons. The output at each node is called its activation or node value. Each link is associated with weight. ANNs are capable of learning, which takes place by altering weight values.
- 6. ANNs Working: In the artificial neural network each arrow represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a weight, an integer number that controls the signal between the two neurons. If the network generates a “well or not well” output, there is no need to adjust the weights. If the network generates a “poor or undesired” output or an error, then the system alters the weights in order to improve subsequent results.
- 7. Artificial Neural Network Architecture: An Artificial Neural Network is defied as a data processing system consisting of a large number of simple highly interconnected processing elements in an inspired by the structure of the cerebral cortex of the brain.
- 8. An Artificial Neural Network structure can be represented using a directed graph. A graph G is an ordered of 2 -tuple (V,E ) consisting of set of V vertices and set of E edges. It is called as digraph or directed graph. Vertices is represented as neurons (inputs, outputs) and the edges is synaptic links.
- 9. Single Layer Feedforward Network: The single layer feedforward network contains two layers input layer and output layer. The input layer neurons receives the input signals. The output layer receives the output signals.
- 10. The synaptic links will carry their weight and connects to each input and output neuron. Such a network is a said to be feedforward in a type acyclic in nature. The input layer transmits the signals to the output layer. Hence this is called as single layer feedforward network.
- 11. Multilayer feedforward network: The multilayer feedforward network has three layer input layer, output layer and intermediate layer is called hidden layer. Hidden layer is called hidden neurons or hidden units.
- 12. One input layer, one output layer and two hidden layer . The input layer neurons are linked to the hidden layer neurons and weight is carried in their links is called input hidden layer weight. The hidden layer neurons are linked to the output layer neurons and the weights are called as hidden output layer weights.
- 13. Recurrent networks: These network differ from feedforward network architecture in the sense that there is atleast feedback loop. There could also be neurons with self-feedback links the output of a neuron is feedback into itself as input.
- 14. Characteristics of neural network: The neural network has mapping capabilities, that is they can map input patterns to their associated output patterns. The neural networks process the capability to generalize. The Neural network are robust systems and are fault tolerant. Recall all patterns from incomplete, or noisy patterns. The neural network can process information in parallel, at high speed, and in a distributed manner.
- 15. APPLICATIONS: Airline Security Control. Investment Management and Risk Control. Prediction of Thrift Failures. Prediction of Stock Price Index. OCR Systems. Industrial Process Control. Data Validation. Risk Management. Target Marketing. Sales Forecasting. Customer Research.
- 16. Classification Of Learning Algorithm:
- 17. Learning Methods. Learning method has three types they are: Supervised learning. Unsupervised learning. Reinforced.
- 18. Supervised Learning. In this learning the input pattern will be trained the output pattern for the target of the desired pattern. We assume that a teacher will be present in the class during the learning process. A comparison will be done between the computed output and corrected output to find the error. The error can be change by network parameter by the improvement of the result performance.
- 19. Unsupervised learning: In this learning method, the target output is not presented in the network that is a teacher will not be presented in the class in the desired pattern. So the system will be discovered by its own knowledge by its input patterns.
- 20. Reinforced learning: In this method a teacher will be present in the class by they won’t correct the output only they will indicate that the output is correct or wrong. In this learning method a reward will be given for the correct answer and the penalty will be given for the wrong answer.
- 21. Hebbian learning: Hebbian learning is based on correlative weight adjustment. The input-output pattern pairs(X i ,Y i) are associated by the matrix W, known as correlation matrix. is transpose of output vector Yi.
- 22. Gradient descent learning: This is based on the minimization of error E defined in terms of weights and the activation function of the network. If Wij is the weight update of the link connecting ith and jth neuron of the two layers Wij is defined as
- 23. Competitive learning: The neurons which respond strongly to input stimuli have their weights updated. When an input pattern is presented, all neurons in the layer compete and the winning neuron undergoes weight adjustment. That “winner takes all “ strategy. Stochastic learning: In this the weights are adjusted in a probabilistic fashion.
- 24. Conclusion: In this paper we discuss about the Artificial neural network, methods and application of Artificial neural network. In this Artificial neural network technology is developing day by day. It is useful for all human that is it will reduce the time of learning and by this it solves our learning problem. We can save more and more time and money in any work. A process of learning speed will be increased and so it is benefit for us. A improvement should be need for day by day in our technology period. We can develop much more algorithms and problems.