Abstract: This paper presents an intelligent tuning method of
microwave filter based on complex neural network and improved
space mapping. The tuning process consists of two stages: the initial
tuning and the fine tuning. At the beginning of the tuning, the return
loss of the filter is transferred to the passband via the error of phase.
During the fine tuning, the phase shift caused by the transmission line
and the higher order mode is removed by the curve fitting. Then, an
Cauchy method based on the admittance parameter (Y-parameter) is
used to extract the coupling matrix. The influence of the resonant
cavity loss is eliminated during the parameter extraction process. By
using processed data pairs (the amount of screw variation and the
variation of the coupling matrix), a tuning model is established by
the complex neural network. In view of the improved space mapping
algorithm, the mapping relationship between the actual model and
the ideal model is established, and the amplitude and direction of the
tuning is constantly updated. Finally, the tuning experiment of the
eight order coaxial cavity filter shows that the proposed method has
a good effect in tuning time and tuning precision.
Abstract: This paper presents a novel approach for the design of
microwave circuits using Adaptive Network Fuzzy Inference
Optimizer (ANFIO). The method takes advantage of direct synthesis
of subsections of the amplifier using very fast and accurate ANFIO
models based on exact simulations using ADS. A mapping from
course space to fine space known as space mapping is also used. The
proposed synthesis approach takes into account the noise and
scattering parameters due to parasitic elements to achieve optimal
results. The overall ANFIO system is capable of designing different
LNAs at different noise and scattering criteria. This approach offers
significantly reduced time in the design of microwave amplifiers
within the validity range of the ANFIO system. The method has been
proven to work efficiently for a 2.4GHz LNA example. The S21 of
10.1 dB and noise figure (NF) of 2.7 dB achieved for ANFIO while
S21 of 9.05 dB and NF of 2.6 dB achieved for ANN.
Abstract: This paper presents an analytical method to solve
governing consolidation parabolic partial differential equation (PDE)
for inelastic porous Medium (soil) with consideration of variation of
equation coefficient under cyclic loading. Since under cyclic loads,
soil skeleton parameters change, this would introduce variable
coefficient of parabolic PDE. Classical theory would not rationalize
consolidation phenomenon in such condition. In this research, a
method based on time space mapping to a virtual time space along
with superimposing rule is employed to solve consolidation of
inelastic soils in cyclic condition. Changes of consolidation
coefficient applied in solution by modification of loading and
unloading duration by introducing virtual time. Mapping function is
calculated based on consolidation partial differential equation results.
Based on superimposing rule a set of continuous static loads in
specified times used instead of cyclic load. A set of laboratory
consolidation tests under cyclic load along with numerical
calculations were performed in order to verify the presented method.
Numerical solution and laboratory tests results showed accuracy of
presented method.