ASSIGNMENT 1 
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Use an expert system shell to implement the query answering system given in the book about 
investing based on investor's risk classification, but make the knowledge 
base more complex.
(decision tree and example is on page 289)
A possible shell that may be used is Exsys (free download for 30 days at www.exsys.com)


END OF ASSIGNMENT 1
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 ASSIGNMENT 2 

PART 1:
Use a  neural network off the selve package, to build a Multilayered,
feedforward, back propagation neural net for the data set given below.
Inputs are speed and distance and output the amount of turn.
Experimentation will determine the best architecture for the net.
Provide hard copy listing: the architecture, activation functions and learning
algorithm that you used, the weight set after training, the training and
testing data sets that were used, and the training and testing errors of
your best network  model.

PART 2:

You are to use the JFS Fuzzy Logic System to implement a fuzzy controler
which determines the amount of left or right turn of the automobile driving wheel (in degrees),
in order for the automobile to follow a contour.
The input fuzzy variables are speed of the automobile and the difference between the distances 
to the contour between the front of the automobile and the rear.
Use experimentation to design the most appropriate fuzzy sets and membership functions,
and test accuracy of your system at the end by using the same error function that you used 
in PART 1.


PART 3:  Compare the NN model that you built with the fuzzy model. Which is more accurate? Can you
explain why?


Data set for both parts:
A run by a human driver yielded these sensor measurements:

speed       distance       turn
10             +5           15
50             +5           30
10             +20          40
50             +20          60
15             -5           -15
50             -5           -30
15             -20          -43
50             -20          -65
70		+5	    20
70		+20	    39
70	       -5	    -24
70	       -20	    -55

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