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Examining the Back Propagation Process up Chapter 6: Understanding the Kohonen Neural Network

Home Introduction to Neural Networks w ith Java Chapter 5: Understanding Back Propagation

SummarySubmitted byjeffheaton on Sun, 12/23/2007 - 21:46

in forward Neural networkGet the entire book!

In this chapter you learned how a feed forw ard back propagation neural network functions. You saw how theJOONEneural netw ork implemented such aneural network. The feed forward back propagation neural network is actually composed of twoneural network algorithms. It is not necessary to alwaysuse "feed forward" and "back propagation" together, but this is usually the case. The term"feed forward" refers to a method by which a neural networkrecognizes a pattern, where as the term"backpropagation" describes a process by which the neural network will be trained.

A feed forward neural network is a network where neurons are only connected to the next layer. There are no connections between neurons in previous

layers or between neurons and themselves. Additionally neurons w ill not be connected to neurons beyond the next layer. As the pattern is processed by afeed forward the bias and connection weights w ill be applied.

Neural networks can be trained using backpropagation. Backpropagation is a formof supervised training. The neural network is presented with the trainingdata, and the results f romthe neural network are compared with the expected results. The difference between the actual results and the expected resultsproduces an error. Backpropagation is a method whereby the weights and input bias of the neural network are altered in a way that causes this error to bereduced.

The feed forward back propagation neural network is a very common network architecture. This neural network architecture can applied to many cases.There are other neural network architectures that may be used. In the next chapter we will examine the Kohonen neural network. The most significantdifference between the Kohonen neural network and the feed forw ard backpropagation neural network that we just examined is the training method. Thebackpropagation method uses a supervised training method. In the next chapter w e will see how an unsupervised training method is used.

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Introduction to Neural

Networks with Java

Chapter 1: Introduction to

Neural NetworksChapter 2: UnderstandingNeural NetworksChapter 3: Using MultilayerNeural NetworksChapter 4: How a MachineLearnsChapter 5: UnderstandingBack Propagation

IntroductionA Feed Forw ard NeuralNetworkJava and ThreadsExamining the FeedForward ProcessExamining the BackPropagation ProcessSummary

Chapter 6: Understanding theKohonen Neural NetworkChapter 7: OCR with the

Kohonen Neural NetworkChapter 8: UnderstandingGenetic AlgorithmsChapter 9: UnderstandingSimulated AnnealingChapter 10: Eluding LocalMinimaAppendix A. JOONEReferenceAppendix B. MathematicalBackgroundAppendix C. CompilingExamples under WindowsAppendix D. CompilingExamples under Linux/UNIX

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