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Sequential composition is associative. That is, it does not matter whether we read 5; T; U as (5; T); U or as 5; (T; U). The statement (5; T); U is executed by first executing 5; T and then executing U; the statement 5; ( T ; 17) is executed by first executing 5 and then executing T; U. Both boil down to executing 5 then T then U. This is a rule that we use in many places, but always without explicit mention of the fact. Sequential composition also has a unit. It is the 'do-nothing' statement. Most often, there is no need to waste ink on doing nothing. Sometimes, we do want to make the operation explicit, in which case we denote it by skip. Doing nothing means leaving the state of the program variables unchanged. So, clearly,

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Analytical solution (meV) El (nieV) E2 (meV) 126.227914 80.111376 53.276432 37.619825 27.884814 21.463972 13.820474 9.629394 166.522007 137.330295 106.557890 83.606781 54.647134 38.282383

11. Design a batch version of the PSO NN training algorithm. 12. What is the main requirement for activation and error functions if gradient descent is used to train supervised neural networks 13. What is the main advantage of using recurrent neural networks instead of feedforward neural networks 14. What is the main advantage in using PUs instead of SUs

skip;5 = 5 = 5;skip ,

Numerical solution E E1 I (meV) E2 (meV) 126.204335 80.087722 53.260451 37.609351 27.877769 21.459068 13.817861 9.627854 167.766634 137.308742 106.535858 83.589149 54.636614 38.275914

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confirming that skip is the (left and right) unit of sequential composition. Doing nothing does not seem a very useful operation to do, so introducing a notation for it does not seem useful either. Like the introduction of zero in the number system, the consideration of extreme cases often meets with resistance

By gathering terms in u(z) on the right hand side, then:

4

1 0: Sequential Composition and Conditional Statements but is, in fact, very important. We will see that the skip statement does have its uses. Formally, { P } skip { Q } means that, beginning in a state satisfying P, the act of doing nothing is guaranteed to terminate in a state satisfying Q. It is thus equivalent to [P=*Q], where the square brackets mean that the statement P=>Q is true in all states. This is the skip rule: [Skip] { P } s k i p { Q } = [P= Q] . (10.5)

Making the transformation, 2Sz > Sz, then gives:

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We recommend that the square brackets are read as 'in all states'. The skip rule is then read as 'beginning in a state satisfying P, execution of skip (i.e. doing nothing) is guaranteed to terminate in a state satisfying Q if, in all states, P implies Q'. In most cases, skip is not explicit (think of it as being written with zero amount of ink). The skip rule is thus most often used in the form: [Weakening] { P } { Q } = [P=>Q] . (10.6)

which is the variable effective-mass shooting equation, and is solved according to the boundary conditions (as in Section 3.1). The effective mass m* can be found at the intermediate points, z 8z/2, by taking the mean of the two neighbouring points at z and z 6z. Clearly, equation 3.53 collapses back to the original form in equation (3.11) when m* is constant. Table 3.4 compares the ground-state and first excited-state energy levels, EI and E-2, respectively, calculated with this extended shooting equation, with the analytical solution from Section 2.6, for a GaAs quantum well surrounded by Gao.8Alo.2As barriers. In this series of calculations, the step length Sz was taken as 1 A and it can be seen from the data in the table that the agreement is very good for both the ground-state energy EI and the first excited-state energy E2 across the range of well widths. The discrepancy between the solutions of the two methods is largest for the excited state of the narrower wells, at which point it is of the order of 1 meV. For

An important feature of NNs is their ability to learn from their environment. 3 covered NN types that learned under the guidance of a supervisor, or teacher. The supervisor presents to the NN learner an input pattern and a desired response. Supervised learning NNs then try to learn the functional mapping between the input and desired response vectors. In contrast to supervised learning, the objective of unsupervised learning is to discover patterns or features in the input data with no help from a teacher, basically performing a clustering of input space. This chapter deals with the unsupervised learning paradigm. Section 4.1 presents a short background on unsupervised learning. Hebbian learning is presented in Section 4.2, while Section 4.3 covers principal component learning, Section 4.4 covers the learning vector quantizer version I, and Section 4.5 discusses self-organizing feature maps.

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