Applications and Science in Soft Computing by T. A. McQueen, A. A. Hopgood, J. A. Tepper, T. J. Allen
By T. A. McQueen, A. A. Hopgood, J. A. Tepper, T. J. Allen (auth.), Dr. Ahamad Lotfi, Dr. Jonathan M. Garibaldi (eds.)
The e-book covers the speculation and alertness of soppy computing strategies particularly; neural networks, fuzzy common sense, evolutionary computing and complicated structures. The ebook is a suite of chosen, edited papers provided on the 4th convention RACS contemporary Advances in delicate Computing held in Nottingham, December 2002. It offers the newest advancements in functions of soppy computing recommendations in addition to advances in theoretical elements of soppy computing.
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Extra info for Applications and Science in Soft Computing
The connections in the networks were then diluted according to the methods described above. Pattern Stability The proportion of fundamental memories that are stable after dilution provides an indicator of the robustness of a model. Networks were trained to below maximum capacity, so all training patterns were fundamental memories prior to dilution. Figs 1 and 2 show the proportion of stable training patterns at various dilutions. Attractor Performance For an associative memory to be effective, the training patterns should be both stable states of the network and attractors in its state space.
In: Proc. of IEEE 1998 Conf. on Electrical and Computer Engineering Waterloo, Vol1 pp313-316. Frank Department of Computer Science, University of Hertfordshire, College Lane, Hatfield, AL10 9AB. uk Abstract. The consequences of two techniques for symmetrically diluting the weights of the standard Hopfield architecture associative memory model, trained using a non-Hebbian learning rule, are examined. This paper reports experimental investigations into the effect of dilution on factors such as: pattern stability and attractor performance.
In Fig. 2a, the number of clusters is plotted for the random baseline and for the data used in experiment 1. It can be observed that ρrmax for the random baseline is smaller than ρmax for the tested data. As per Eq. 5, this is an indication that cluster tendency is not caused by random structure in the data. In Fig. 2b, two other data sets are compared to the random baseline. For these data sets, ρrmax > ρmax, which means that clustering tendency is caused by mere chance clustering. org. ch/hotbits/) ).