E$3\mathrm{.}1$ In this chapter we have designed three different neural networks to distin$-$ guish between apples and oranges, based on three sensor measurements $($shape$,$ texture and weight$).$ Suppose that we want to distinguish between bananas and pineapples: P $=$ E $1($Banana$)$ P$2=-1($Pineapple$)$ i$.$ Design a perceptron to recognize these patterns. ii$.$ Design a Hamming network to recognize these patterns. iii. Design a Hopfield network to recognize these patterns. iv$.$ Test the operation of your networks by applying several different in$-$ put patterns. Discuss the advantages and disadvantages of each network. E$3\mathrm{.}2$ Consider the following prototype patterns. P $=10\mathrm{.}5$ P$2=$ i$.$ Find and sketch a decision boundary for a perceptron network that will recognize these two vectors. ii$.$ Find weights and bias which will produce the decision boundary you found in part i$,$ and sketch the network diagram. iii. Calculate the network output for the following input. Is the network response $($decision$)$ reasonable? Explain. P iv$.$ Design a Hamming network to recognize the two prototype vectors above. v$.$ Calculate the network output for the Hamming network with the in$-$ put vector given in part in$,$ showing all steps. Does the Hamming network produce the same decision as the perceptron? Explain why or why not. Which network is better suited to this problem? Explain.