Neural Network
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Neural Network

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    Neural Network Neural Network Presentation Transcript

    • NEURAL NETWORK Submitted by : Abhishek Sasan(500901515) Laleet Grover() Munish Kumar(500901505)
    • Content Definition Examples Types of Neural Networks Selection of NN Areas where NN is useful Applications Advantages Limitations SNNS
    • DefinitionA neural network is a computationalmethod inspired by studies of the brain andnervous systems in biological organisms.A Computing system made of a number ofsimple, highly interconnected processingelements, which process information by theirdynamic state response to external input.
    • Example :Single Neuron
    • Example :Three Layers Neural Net
    • Neural NetworkThey can be distinguished by: their type (feed forward or feed back) their structure the learning algorithm they use
    • Types of Neural Network
    • Single Layer Feed forward Network
    • Multi -Layer Feed forward Network
    • Feed Back Network
    • Selection of Neural Nets
    • Perceptron
    • Multi-Layer-Perceptron
    • Back propagation Net
    • Hopfield Net
    • Kohonen Feature Map
    • How Do Neural Networks Work ?The output of a neuron is a function of theweighted sum of the inputs plus a bias i1 w1 i2 w2 Output = f(i1w1 + Neuron i2w2 + i3w3 + bias) i3 w3 Bias
    • Areas where Neural Net May be Useful Pattern association Pattern classification Regularity detection Image processing Speech analysis Optimization problems
    • Three Main ApplicationsConcurrent simulation, where results of an ANNmodel are compared with results of a less realistic butvalidated common model to avoid a non expectedbehavior of the Neural-Net.ANN as sub-components of a global model, to modelsubsystems that would be hard to model commonlybecause of a lack of understanding.Adaptive models, "models that can learn", accordingto an error feedback such model would be able to adaptruntime to situations that hasnt been taken into account.
    • Why Use Neural NetworksAbility to learn : NN’s figure out how to perform their function on theirownDetermine their function based only upon sample inputsAbility to generalizei.e. produce reasonable outputs for inputs it has not beentaught how to deal with
    • Advantages : Neural NetworkHandle partial lack of system understandingCreate adaptive models (models that canlearn)
    • Limitations The operational problem encounteredwhen attempting to simulate the parallelismof neural networks Instability to explain any results that theyobtain
    • Neural Network Software Neural network software is used to stimulate, research,develop and apply artificial neural networks, biologicalneural networks Simulators usually have some form of built-in visualization to monitor the training process