ahyeek - Professional Profile

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This Tapped Delay Line (TDL) neural network is using the previous value on a graph to train it and used to predict a furture value. Given points from a graph as (x1, y1), (x2, y2), .... , (x(n-1), y(n-1)), TDL Nnet with (n-1) delay step is able to predict (x(n), y(n)) by giving it the (n-1)previous values. For each iteration, the Neural Net will get trained again and again in real time by suplying the actual value to the Neural Net. Hence, we consider TDL is a real time neural network because the training mechanism can be done in real time.

When you try to run the simulation application, try to observe that the average error values will decrease when the iteration increase. You can try to use differerent data set and observe how the ADALINE TDL neural net perform its prediction. The weight values of the neural net will become stable while the error of prediction value is low. Observing that the predicted value (graph blue in color) is not overlap with the actual value (graph in yellow color) at the beginining. However, it will slowly overlap each and other after some iteration. This is because the neural net has been trained and recongnize the pattern of the given graph.

You will observe the predicted values from the simulation will become more and more accurate after some iteration. The average error value will reduce while the iteration increase. The neural network will become more and more 'intelligent' in predicting the next value of the graph after it has been thought for some time. You can choose different graph set to test out the Nnet using the simulator. By noting down the result, you actually can compare what properties values of the neural net are the best setting for obtaining fastest and lowest error value. The "Delay Step" and "Learning Rate" are the properties that determine how fast the neural network are able to be trained and how accurate the predicted value.

http://silyeek-tech.blogspot.com/2007/04/adaline-tdl-neural-network-simulation.html[^]

Since 1998 ...

Year: 2005 - 2006
Programming Language & Tools: Microsoft .NET C#
Source Code: Email the author: ahyeek@gmail.com
Application For Test: Download
This simulation was developed to learn Genetic Algorithm and coded in Ms. Net C#. The problem that the author faced was to solve the vehicle routing. In order to implement and visualize how GA perform in solving the problem, the simulator was impemented with a random generated map. Users can actually provide the number of locations he/she wants and how many roads connect to each location, then the map generator will generate a map with the corresponding setting.

Several parameters need to be provided before performing the GA to solve the problem. The parameters are basically the GA needed parameters, such as Population size, Cross-Over Rate, Mutation Rate and Number of Generation. User need to determine the source and destination on the map before simulate the solutions. The system finally will give a path that connect the source and destination location as well as the distant and time using the path. The goal is to get the shortest and fastest route for travel from source to destination.

Furthermore, the simulator actually build in another algorithm - Dijikstra Algorithm. This algoritm is the best and fastest algo in solving shortest path problem. It's actually used to compare wtih GA in solving a specified situation. Simulator also build in with all potential path generation mechanism, but it depend on maps and the source and destnation location that user choose. Sometimes, it will take long time to get all the posible paths generated. However, this mechanism is actually implemented in a thread manner that user actually can generate the potential path and let the simulation run synchronizely.

There are a lots of other useful an interesting features implemented in the simulator and the author think it will be too much to state here. So, let download the system and try it! You will discover more....

http://silyeek-tech.blogspot.com/2006/03/genetic-algorithm-ga-in-solving.html[^]

Since 1998 ...