Quasi-Newton Method for DSPSL Bandpass Filter Design

2011 ◽  
Vol 219-220 ◽  
pp. 778-781
Author(s):  
Shan Wang
Keyword(s):  
Neural Network ◽  
Newton Method ◽  
Bandpass Filter ◽  
Filter Design ◽  
Nonlinear Circuits ◽  
Analysis Techniques ◽  
The Neural Network ◽  
Electro Magnetic ◽  
Quasi Newton ◽  
Good Agreement

A method based on neural-network is developed and applied to analyze the DSPSL filter design. Through the neural network analysis of nonlinear circuits, it can enhance the efficiency of electro-magnetic (EM) analysis techniques for DSPSL filter design. Quasi-Newton method is adopted, which has shorter training period and the faster convergence. A good agreement between ANN results and EM simulations verifies the validity of this proposed MLPNN model.

Compact dual-mode microstrip bandpass filter based on slotted square patch resonator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Karthie S. ◽  
Zuvairiya Parveen J. ◽  
Yogeshwari D. ◽  
Venkadeshwari E.
Keyword(s):  
Bandpass Filter ◽  
Filter Design ◽  
Center Frequency ◽  
Dual Mode ◽  
Content Type ◽  
Transmission Zeros ◽  
Compact Size ◽  
Mode Response ◽  
Better Than ◽  
Good Agreement

Purpose The purpose of this paper is to present the design of a compact microstrip bandpass filter (BPF) in dual-mode configuration loaded with cross-loop and square ring slots on a square patch resonator for C-band applications. Design/methodology/approach In the proposed design, the dual-mode response for the filter is realized with two transmission zeros (TZs) by the insertion of a perturbation element at the diagonal corner of the square patch resonator with orthogonal feed lines. Such TZs at the edges of the passband result in better selectivity for the proposed BPF. Moreover, the cross-loop and square ring slots are etched on a square patch resonator to obtain a miniaturized BPF. Findings The proposed dual-mode microstrip filter fabricated in RT/duroid 6010 substrate using PCB technology has a measured minimum insertion loss of 1.8 dB and return loss better than 24.5 dB with a fractional bandwidth (FBW) of 6.9%. A compact size of 7.35 × 7.35 mm2 is achieved for the slotted patch resonator-based dual-mode BPF at the center frequency of 4.76 GHz. As compared with the conventional square patch resonator, a size reduction of 61% is achieved with the proposed slotted design. The feasibility of the filter design is confirmed by the good agreement between the measured and simulated responses. The performance of the proposed filter structure is compared with other dual-mode filter works. Originality/value In the proposed work, a compact dual-mode BPF is reported with slotted structures. The conventional square patch resonator is deployed with cross-loop and square ring slots to design a dual-mode filter with a square perturbation element at its diagonal corner. The proposed filter exhibits compact size and favorable performance compared to other dual-mode filter works reported in literature. The aforementioned design of the dual-mode BPF at 4.76 GHz is suitable for applications in the lower part of the C-band.

Research on BP Neural Network Algorithm Based on Quasi-Newton Method

2014 ◽  
Vol 686 ◽  
pp. 388-394 ◽  
Author(s):  
Pei Xin Lu
Keyword(s):  
Neural Network ◽  
Empirical Analysis ◽  
Newton Method ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Newton Methods ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quasi Newton ◽  
Improved Bp Algorithm

With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi-Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the improved BP algorithm is better.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

scholarly journals The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

2016 ◽  
Author(s):  
Paolo Sanò ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Anna Cinzia Marra ◽  
Francesco Di Paola ◽  
...  
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Passive Microwave ◽  
Precipitation Radar ◽  
Water Vapor Absorption ◽  
The Neural Network ◽  
Trmm Pr ◽  
The Cross ◽  
Cross Track ◽  
Good Agreement

Abstract. The objective of this paper is to describe the development and evaluate the performance of a totally new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track ATMS radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered . The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area, the statistical analysis was carried out for a two-year (2013-2014) dataset of coincident observations, over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with general better estimate of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

A Step-by-Step Approach to Bandpass/Channel Filter Design

2021 ◽  
Vol 10 (2) ◽  
pp. 1-14
Author(s):  
Augustine O. Nwajana
Keyword(s):  
Return Loss ◽  
Bandpass Filter ◽  
Filter Design ◽  
Reference Source ◽  
Fractional Bandwidth ◽  
Microstrip Technology ◽  
Centre Frequency ◽  
Device Size ◽  
Channel Filter ◽  
Good Agreement

This paper presents a step-by-step approach to the design of bandpass/channel filters. The chapter serves as a reference source to microwave stakeholders with little or no filter design experience. It should help them design and implement their first filter device using the microstrip technology. A 3-pole Chebyshev bandpass filter (BPF) with centre frequency of 2.6 GHz, fractional bandwidth of 3%, passband ripple of 0.04321 dB, and return loss of 20 dB has been designed, implemented, and simulated. The designed filter implementation is based on the Rogers RT/Duroid 6010LM substrate with a 10.7 dielectric constant and 1.27 mm thickness. The circuit model and microstrip layout results of the BPF are presented and show good agreement. The microstrip layout simulation results show that a less than 1.8 dB minimum insertion loss and a greater than 25 dB in-band return loss were achieved. The overall device size of the BPF is 18.0 mm by 10.7 mm, which is equivalent to 0.16λg x 0.09λg, where λg is the guided wavelength of the 50 Ohm microstrip line at the filter centre frequency.

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