Content Definition Examples Types of Neural Networks Selection of NN Areas where NN is useful Applications Advantages Limitations SNNS
DefinitionA 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.
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 ApplicationsConcurrent 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 NetworksAbility to learn : NN’s figure out how to perform their function on theirownDetermine their function based only upon sample inputsAbility to generalizei.e. produce reasonable outputs for inputs it has not beentaught how to deal with
Advantages : Neural NetworkHandle partial lack of system understandingCreate 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
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