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Extra info for Analytic sets. London school 1978
The hidden units are connected to three context units. In total, ve units feed into the hidden layer. 3. 3: Training an Elman network to control an object. The solid line depicts the desired trajectory x d the dashed line the realised trajectory. The third line is the error. 50 CHAPTER 5. RECURRENT NETWORKS feed-forward network with sliding window input. We tested this with a network with ve inputs, four of which constituted the sliding window x;3 , x;2 , x;1 , and x0 , and one the desired next position of the object.
Here, however, the units are binary-valued and are updated stochastically and asynchronously. , 1985). This algorithm works as follows. First, the input and output vectors are clamped. The network is then annealed until it approaches thermal equilibrium at a temperature of 0. It then runs for a xed time at equilibrium and each connection measures the fraction of the time during which both the units it connects are active. This is repeated for all input-output pairs so that each connection can measure hyj yk iclamped , the expected probability, averaged over all cases, that units j and k are simultaneously active at thermal equilibrium when the input and output vectors are clamped.
It appears that, in practice, this algorithm usually converges. There exist cases, however, where the algorithm remains oscillatory (try to nd one)! The second problem stated above can be alleviated by applying the Hebb rule in reverse to the spurious stable state, but with a low learning factor (Hop eld, Feinstein, & Palmer, 1983). Thus these patterns are weakly unstored and will become unstable again. 1 can be generalised by allowing continuous activation values. Here, the threshold activation function is replaced by a sigmoid.
Analytic sets. London school 1978 by Rogers