By Dines Bjørner (auth.), P. Dembiński (eds.)
Read Online or Download Mathematical Foundations of Computer Science 1980: Proceedings of the 9th Symposium Held in Rydzyna, Poland, September 1–5, 1980 PDF
Similar mathematics books
- Advanced Calculus Demystified
- Spectral Theory of Linear Differential Operators and Comparison Algebras
- Curvature and Topology of Riemannian Manifolds. Proc. Taniguchi Symposium, Katata, 1985
- Siegel's modular forms and dirichlet series. Course given at the University of Maryland, 1969-1970
- Signale und Systeme: Einführung in die Systemtheorie
- The Contest Problem Book IV
Additional resources for Mathematical Foundations of Computer Science 1980: Proceedings of the 9th Symposium Held in Rydzyna, Poland, September 1–5, 1980
Generally, for a given application, the architecture of an RNN is either chosen randomly or based on user’s experience. The user then tries a large number of structures for the selected architecture and the parameters for each of these structures. The difﬁculty in making optimal choices for these properties has limited the application of RNNs in spite of its vast potential. Some details about the determination of these properties of an RNN will be provided in the coming sections along with possible issues for future work.
Xn is a fuzzy subset on the Cartesian product space X1 Â X2 Â Á Á Á Â Xn and can be denoted by R(X1, X2, . . , Xn) as ð RðX1 , X2 , . . , Xn Þ ¼ mR ðx1 , x2 , . . , xn Þ=ðx1 , x2 , . . , xn Þ (2:28) X1 ÂX2 ÂÁÁÁÂXn where mR (x1, x2, . . , xn) is a MF of the relation R, which represents the degree of association (correlation) among the elements of the different domain Xi. It is a mapping from the Cartesian space X1 Â X2 Â Á Á Á Â Xn onto a continuous unit interval [0, 1] as R: X1 Â X2 Â Á Á Á Â Xn !
F½mA0 ðxÞ ^ mB0 ðyÞ ^ mRl ðx, y, zÞgÞ x,y,z x,y ¼ [ ðf½mA0 ðxÞ ^ mB0 ðyÞ mR1 ðx, y, zÞg, .. ,f½mA0 ðxÞ ^ mB0 ðyÞ mRl ðx, y, zÞgÞ x,y,z l ¼ [ f½mA0 ðxÞ ^ mB0 ðyÞ mRi ðx, y, zÞg i¼1 ß 2008 by Taylor & Francis Group, LLC. (2:65) where mRi(x, y, z) ¼ mAi(x) ^ mBi(y) ^ mCi(z). If the input variables are fuzzy singletons as A0 ¼ x0 and B0 ¼ y0, then the ﬁring strength ai of the ith rule is expressed as ai ¼ mAi(x0) ^ mBi(y0), which is a measure of the contribution of the ith rule to the resultant fuzzy output.