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1. Implement and test a LVQ-I network to distinguish between different alphabetical characters of different fonts. 2. Explain why it is necessary to retrain a supervised NN on all the training data, including any new data that becomes available at a later stage. Why is this not an issue with unsupervised NNs 3. Discuss an approach to optimize the LVQ-I network architecture. 4. How can PSO be used for unsupervised learning 5. What is the main difference between the LVQ-I and SOM as an approach to cluster multi-dimensional data 6. For a SOM, if the training set contains PT patterns, what is the upper bound on the number of neurons necessary to fit the data Justify your answer. 7. Explain the purpose of the neighborhood function of SOMs. 8. Assuming a Gaussian neighborhood function for SOMs, what can be done to reduce the number of weight updates in a sensible way 9. Explain how a SOM can be used to distinguish among different hand gestures. 10. Discuss a number of ways in which the SOM can be adapted to reduce its computational complexity.

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Next consider G(a) as defined in equation (6.34), then:

13.4 Summary

and so performing the differentiation and substituting r' for rj_ as above, then:

Summary

cosh 9, then:

5

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This chapter has shown how bound functions and invariant properties are used in the design of loops. The clear, mathematical formulation of an invariant is an invaluable aid to avoiding errors when designing loops. For example, formulating the function of the delimiters of array segments helps to avoid the so-called 'oneoff' errors that can plague programs (and cause systems to crash!). This chapter completes our introduction to the principles of program construction. Rules have been formulated for the design of assignment statements, sequential composition of statements, conditional statements and loops. Each of the rules expresses formally the creative element of the use of the particular type of statement. For conditionals, there is a creative element involved in deciding how to split the problem into different cases. In the case of sequential composition, the creative element is in deciding what intermediate condition should be satisfied after the first statement in the sequence is executed. These problem-solving strategies will probably ring a bell of familiarity with the reader. In the case of loops, the creative element is the design of a suitable invariant property. This strategy may be quite new to you. But, its unfamiliarity should not put you off. It is an important skill that is invaluable in program construction. The skill can only be acquired with practice and perseverance. Make the necessary time, and your effort will be well rewarded.

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Making the further substitution, w = exp ( 0], then dO = dw/w and noting that:

then finally:

Knuth's books (1968, 1969) are a mine of historical information on the development of algorithms. For a history of methods of calculating powers (briefly mentioned in Section 13.3.3) see Knuth (1969, pp. 398-422). It is worth looking at Knuth's discussion of the 'SX' method for computing powers, which was written before the importance of invariants had been clearly identified in the development of algorithms. What is interesting is that his (textual) description of the algorithm is highly operational, making it extremely difficult to understand why the method works (at least in my view), but the algorithm itself does include a comment which is precisely the invariant property! The chapters that follow provide more extensive examples of program construction. Other texts that practise 'correct-by-construction' methods for developing non-trivial algorithms are Gries (1981), Dijkstra and Feijen (1984), Kaldewaij (1990), Morgan (1990) and van de Snepscheut (1993). Consult these texts for yet more examples.

Several neural networks have been developed for both the supervised and the unsupervised learning paradigms. While these NNs were seen to perform very well in their respective application fields, improvements have been developed by combining supervised and unsupervised learning. This chapter discusses two such learning algorithms, namely the learning vector quantizer-II in Section 5.1 and radial basis function NNs in Section 5.2.

An analogous argument shows that G(a) appearing in B is exactly equal to this form. Next consider evaluation of the integral J(a) as defined in equation (6.40). With this aim, note that: This then gives the following:

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