The present paper serves three purposes: (1) To introduce a relatzvely new network computer design that is capable o I solving a set o] twenty algebraic equations expressed by the dimensionless relation ri:~-J+ L h'-t ~ohere ~j and d~. are constants depending on the nature o] the problem, and 9 may
Theory of an iterative, two mode transmission network
β Scribed by C. R. Skipping; B. N. Moore; M. E. Oakes
- Publisher
- John Wiley and Sons
- Year
- 1981
- Tongue
- English
- Weight
- 793 KB
- Volume
- 9
- Category
- Article
- ISSN
- 0098-9886
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
A general investigation of the nature of an iterative, LC lattice structure, shown to function as a transmission channel capable of carrying two distinct wave modes, is carried out. Both a uniform system and an inhomogeneous case where the capacitor values vary slowly down the lattice structure are considered. Emphasis is placed on a formal and numerical investigation of basic wave properties and their significance with regard to signal transmission, crosstalk suppression, plus the particularly interesting phenomenon of one mode converting to the other which is found to occur in the inhomogeneous case. Potential uses of the lattice such as simulated wave studies, among others, are suggested. Relations for termination impedances are derived, and special cases involving different combinations of excitation and termination conditions are considered.
π SIMILAR VOLUMES
Part II 2 ## ITERATIVE IMPEDANCES The concept of "iterative impedance" is intrinsically related to those of "load impedance" and "input impedance," which we now define. Definition 15. A transmission network t' = xt -t-r will be said to be terminated in a load impedance ZL (load admittance YL) pro
## Abstract A new approach for induction motor drive control is presented in this paper. The new scheme is based on the direct application of an artificial neural network, trained with sliding mode control, into the feedback control system. Neural network learning is implemented with an onβline ada