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-A FUNCTION- LEARNING BACKPROPAGATION NETWORK -----------------------------------------------------------------------------------------------note that the submodel definition does not depend on nx, ny, nv -ARRAY x$[1], y$[1], v$[1], W1$[1, 1], W2$[1, 1] SUBMODEL NET2(x$, y$, v$, W1$, W2$) Vector v$ = tanh(W1$ * x$) Vector y$ = W2$ * v$ end ---------------------------------------------------------------------------------------------nx = 1 | ny = 1 | nv = 5 | -nv is the number of hidden neurons -ARRAY x[nx] + x0[1] = xx | x0[1] = 1 | -introduce bias ARRAY v[nv], y[ny], target[ny], error[ny], delta2[nv] ARRAY WW1[nv, nx + 1], W2[ny, nv], Dww1[nv, nx + 1], Dw2[ny, nv] --random initial weights for i = 1 to nv WW1[i, 1] = 02 * ran() | WW1[i, 2] = 02 * ran() | W2[1, i] = 02 * ran() next ----------------------------------------------------------------------------------------------set experiment parameters lrate1 = 1 | lrate2 = 03 | mom1 = 01 | mom2 = 01 scale = 05 | NN = 10000 -for i = 1 to 3 | drun | next | -training runs lrate1 = 04 | lrate2 = 015 | -decrease lrate for i = 1 to 10 | drun | next -write "type go for a recall run" | STOP drun RECALL -----------------------------------------------------------------DYNAMIC -----------------------------------------------------------------x[1] = ran() | target[1] = 04 * sin(4 * x[1]) invoke NET2(xx, y, v, WW1, W2) ------------------------------------------------training Vector error = target - y Vector delta2 = W2% * error * (1 - v^2) MATRIX Dww1 = lrate1 * delta2 *xx + mom1 * Dww1 MATRIX Dw2 = lrate2 * error * v + mom2 * Dw2 DELTA WW1 = Dww1 | DELTA W2 = Dw2 ------------------------------------------------------------------label RECALL x[1] = ran() | target[1] = 04 * sin(4 * x[1]) invoke NET2(xx, y, v, WW1, W2) Vector error = target - y

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The rst constraint represents the requirement that the aggregate bandwidth must be smaller than the client access bandwidth. This allows the client to receive all Type-I channels simultaneously. The second constraint represents the requirement that we should allocate as many channels as the client access bandwidth will allow maximizing utilization of the client access bandwidth. It is worth noting that if we remove the client access bandwidth constraint C, the number of Type-I channels n 1 will simply equal to N , i.e., all channels are of Type-I. In this special case, the bandwidth partition scheme in equation (18.5) will be identical to the Poly-harmonic Broadcasting scheme [6]. Therefore, Poly-harmonic Broadcasting can be considered as a special case of Consonant Broadcasting when there is no client access bandwidth constraint. Figure 18.1 illustrates the operation of Type-I channels (channels 0 to 3). When a client enters the system to start a new media stream, it will immediately start caching data from all Type-I channels simultaneously. The client can start playback after a latency of T seconds as the rst media segment L 0 will be completely received by then. In general, let t0 be the time the client enters the system, and let ci be the playback time for media segment L i , which can be computed from ci = t0 + (m + i) U, i = 0, 1, . . . , n 1 1 (18.7)

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As the client caches all Type-I channels immediately at time t0 , it will have completely received media segment L i by the time si given by si = t0 + (L b)/N , b/(m + i) i = 0, 1, . . . , n 1 1 L =U N (18.8)

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FIGURE 6-4a Training program and recall test for a two-layer backpropagation network learning the sine function by mean-square regression of a random input on the target function 04 * sin(4 * x[1]) The network (but in this case not the training program) is defined as a convenient submodel that can be stored and reused with different input, output, and hidden-layer dimensions nx, ny, nv xx, WW1, and Dww1 are bias-augmented arrays (Section 6-2)

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