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Technique implementing human nervous system for better computing

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- 1. NEURAL NETWORKSM.H.Vinay Charan 08P71A12C0
- 2. What is a Neural Network? An artificial neural network (ANN), often just called a "neural network" (NN), is a mathematical model or computational model based on biological ` neural networks, in other words, is an emulation of biological neural system.
- 3. Data Mining Data mining is the term used to describe the process of extracting value from a database. Four things are required to data-mine effectively: ` • High-quality data. • “right” data. • Adequate sample size. • Right tool.
- 4. Why Neural Network?• Neural networks are useful for data mining and decision- support applications.• People are good at generalizing from experience. `• Computers excel at following explicit instructions over and over.• Neural networks bridge this gap by modeling, on a computer, the neural behavior of human brains.
- 5. ANN Characteristics• Neural networks are useful for pattern recognition or data classification, through a learning process.• Neural networks simulate biological systems, ` where learning involves adjustments to the synaptic connections between neurons.
- 6. Anatomy of ANN• Neural Networks map a set of Input 0 Input 1 ... Input n input-nodes to a set of output- nodes Neural Network• Number of inputs/outputs is ` variable Output 0 Output 1 ... Output m• The Network itself is composed of an arbitrary number of nodes with an arbitrary topology. 6
- 7. Biological Background• A neuron: many-inputs / one-output unit• Output can be excited or not excited• Incoming signals from other neurons determine if the ` neuron shall excite ("fire")• Output subject to attenuation in the synapses, which are junction parts of the neuron
- 8. Neural Network Topologies This is of two types: Feed Forward Neural Networks: • Unidirectional, No feedback, No cycles. ` Recurrent Network: • Bi-directional, feedback.
- 9. Training Of ANN A neural network has to be configured such that the application of a set of inputs produces the desired set of outputs. ` „Train‟ the neural network by feeding it teaching patterns and letting it change its weights according to some learning rule.
- 10. Neural Network in Data Mining• Feed Forward Neural Network:The simplified process for training a FFNN is as follows:1. Input data is presented to the network and propagated ` through the network until it reaches the output layer. This forward process produces a predicted output.2. The predicted output is subtracted from the actual output and an error value for the networks is calculated.
- 11. Neural Network in Data Mining3. The neural network then uses supervised learning, which in most cases is back propagation, to train the network. Back propagation is a learning algorithm for adjusting the ` weights. It starts with the weights between the output layer PE‟s and the last hidden layer PE‟s and works backwards through the network.4. Once back propagation has finished, the forward process
- 12. Neural Network in Data Mining• Back Propagation: Is a common method of teaching artificial neural networks how to perform a given task. The back ` propagation algorithm is used in layered feedforward ANNs. The back propagation algorithm uses supervised learning, which means that we provide the algorithm
- 13. Neural Network in Data MiningTechnique used:1. Present a training sample to the neural network.2. Compare the networks output to the desired output from that sample. Calculate the error in each output neuron. `3. For each neuron, calculate what the output should have been, and a scaling factor, how much lower or higher the output must be adjusted to match the desired output. This is the local error.
- 14. Neural Network in Data Mining4. Adjust the weights of each neuron to lower the local error.5. Assign "blame" for the local error to neurons at the ` previous level, giving greater responsibility to neurons connected by stronger weights.6. Repeat the steps above on the neurons at the
- 15. Basics of a Node Input 0 Input 1 ... Input n• A node is an W0 W1 ... Wn element which Wb + + performs a function ` fH(x) Connection y = fH(∑(wixi) + Wb) Output Node 15
- 16. Simple Perceptron• Binary logic Input 0 Input 1 application W0 W1• fH(x) [linear threshold] ` Wb +• Wi = random(-1,1) fH(x)• Y = u(W0X0 + W1X1 Output + Wb) 16
- 17. Algorithm1. Initialize the weights in the network (often randomly)2. repeat * for each example e in the training set do 1. O = neural-net-output(network, e) ; forward pass 2. T = teacher output for e 3. Calculate error (T - O) at the output units 4. Compute ‘delta_wi’ for all weights ` from hidden layer to output layer ; backward pass 5. Compute delta_wi for all weights from input layer to hidden layer ; backward pass continued 6. Update the weights in the network * end3. until all examples classified correctly orstopping criterion satisfied4. return(network)
- 18. Advantages• High Accuracy: Neural networks are able to approximate complex non-linear mappings• Noise Tolerance: Neural networks are very flexible with respect to incomplete, missing and noisy data. `• Independence from prior assumptions: Neural networks do not make a priori assumptions about the distribution of the data, or the form of interactions between factors.• Ease of maintenance: Neural networks can be updated with fresh data, making them useful for dynamic environments.
- 19. Applications• Finance.• Marketing.• Human Resource. `• Accounting.
- 20. Design Problems• There are no general methods to determine the optimal number of neurons necessary for solving any problem. `• It is difficult to select a training data set which fully describes the problem to be solved.
- 21. Improving ANN• Designing Neural Networks using Genetic Algorithms.• Neuro-Fuzzy Systems. `
- 22. Conclusion There is rarely one right tool to use in data mining; it is a question as to what is available and what gives the “best” results. Many ` articles, in addition to those mentioned in this paper, consider neural networks to be a promising data mining tool
- 23. Thank you

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